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source/JiRack_H12_L12_V50257_D768_MSL8192_FF768x4.py

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+# Copyright (c) 2025 CMS Manhattan
+# All rights reserved.
+# Author: Konstantin Vladimirovich Grabko
+# Email: grabko@cmsmanhattan.com
+# Phone: +1(516)777-0945
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+#
+# Additional terms:
+# Any commercial use or distribution of this software or derivative works
+# requires explicit written permission from the copyright holder.
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, List
+import math
+from pathlib import Path
+
+# ========================================
+# Model Configuration (GPT-2 Base Style)
+# ========================================
+VOCAB_SIZE = 50257
+MODEL_DIM = 768
+NUM_HEADS = 12
+NUM_LAYERS = 12
+MAX_SEQ_LEN = 8192
+FFN_HIDDEN_DIM = 4 * MODEL_DIM
+HEAD_DIM = MODEL_DIM // NUM_HEADS # 768 / 12 = 64
+
+# ИСПРАВЛЕНИЕ: Проверка устройства
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif hasattr(torch, 'hip') and torch.hip.is_available():
+    device = torch.device("cuda") 
+else:
+    device = torch.device("cpu")
+
+# -------------------------------
+# Learned Positional Embedding
+# -------------------------------
+class LearnedPositionalEmbedding(nn.Module):
+    def __init__(self, max_seq_len: int, embed_dim: int):
+        super().__init__()
+        self.pos_emb = nn.Parameter(torch.zeros(max_seq_len, embed_dim))
+
+    def forward(self, x: torch.Tensor, pos_offset: int = 0) -> torch.Tensor:
+        seq_len = x.size(1)
+        # УДАЛЕНА Python-проверка для JIT-совместимости, если требуется
+        pos = self.pos_emb[pos_offset : pos_offset + seq_len]
+        return x + pos.unsqueeze(0)
+
+
+# -------------------------------
+# MultiHeadAttention (MHA)
+# -------------------------------
+class MultiHeadAttention(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.q_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.k_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.v_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.out_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.scale = HEAD_DIM ** -0.5
+
+    def forward(self, x: torch.Tensor, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        B, T, D = x.shape
+        
+        q = self.q_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+
+        # 1. KV-кеш
+        seqlen_k_new = k.size(2)
+        pos_offset = 0
+        if past_kv is not None:
+            past_k, past_v = past_kv
+            k = torch.cat([past_k, k], dim=2)
+            v = torch.cat([past_v, v], dim=2)
+            pos_offset = past_k.size(2)
+        
+        seqlen_k = k.size(2)
+        new_kv = (k, v)
+
+        # 2. Расчет внимания
+        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+
+        # 3. Маскирование
+        if T == seqlen_k_new:
+            causal_mask_present = torch.tensor(seqlen_k > 0, dtype=torch.bool, device=x.device)
+            if causal_mask_present.item():
+                mask = torch.full((T, seqlen_k), float("-inf"), device=x.device, dtype=attn.dtype)
+                current_causal_mask = torch.tril(torch.ones(T, T, device=x.device, dtype=torch.bool))
+                
+                mask[:, :pos_offset] = 0.0
+                mask[:, pos_offset : pos_offset + T].masked_fill_(~current_causal_mask, float('-inf'))
+                
+                attn = attn + mask[None, None, :, :]
+
+        # 4. Выход
+        attn = F.softmax(attn, dim=-1)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).contiguous().view(B, T, D)
+        out = self.out_proj(out)
+
+        return out, new_kv
+
+
+# -------------------------------
+# FeedForward (GELU, GPT-style)
+# -------------------------------
+class FeedForward(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.c_fc = nn.Linear(MODEL_DIM, FFN_HIDDEN_DIM, bias=False)
+        self.c_proj = nn.Linear(FFN_HIDDEN_DIM, MODEL_DIM, bias=False)
+
+    def forward(self, x):
+        return self.c_proj(F.gelu(self.c_fc(x), approximate='tanh'))
+
+
+# -------------------------------
+# Transformer Block (Post-Norm, GPT-style)
+# -------------------------------
+class TransformerBlock(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.attn = MultiHeadAttention()
+        self.ffn = FeedForward()
+        self.norm1 = nn.LayerNorm(MODEL_DIM, eps=1e-5) 
+        self.norm2 = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+
+    def forward(self, x, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        attn_out, new_kv = self.attn(self.norm1(x), past_kv)
+        x = x + attn_out
+        x = x + self.ffn(self.norm2(x))
+        return x, new_kv
+
+
+# -------------------------------
+# Главная модель GPTPyTorch (L=12, H=12)
+# -------------------------------
+class GPTPyTorch(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.token_emb = nn.Embedding(VOCAB_SIZE, MODEL_DIM)
+        self.pos_emb = LearnedPositionalEmbedding(MAX_SEQ_LEN, MODEL_DIM)
+        self.blocks = nn.ModuleList([TransformerBlock() for _ in range(NUM_LAYERS)])
+        self.ln_f = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+        self.lm_head = nn.Linear(MODEL_DIM, VOCAB_SIZE, bias=False)
+        
+        signature = "Konstantin V Gbabko .  original author © 2025"
+        bytes_tensor = torch.tensor([ord(c) for c in signature], dtype=torch.uint8)
+        self.register_buffer("konstantin_gbabko_proof_of_authorship", bytes_tensor)
+        self.register_buffer("konstantin_gbabko_birth_date", torch.tensor([20251126], dtype=torch.int64))
+
+        self.lm_head.weight = self.token_emb.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            std = 0.02 / math.sqrt(2 * NUM_LAYERS) if isinstance(module, nn.Linear) and module.out_features == MODEL_DIM else 0.02
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.zeros_(module.bias)
+            nn.init.ones_(module.weight)
+
+    def forward(self, input_ids, past_kv: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None):
+        B, T = input_ids.shape
+        x = self.token_emb(input_ids)
+
+        pos_offset = 0
+        if past_kv is not None and past_kv[0] is not None:
+            pos_offset = past_kv[0][0].size(2)
+            
+        x = self.pos_emb(x, pos_offset=pos_offset)
+
+        # 🎯 ИСПРАВЛЕНИЕ: Инициализация кеша (если T > 1 ИЛИ кеш передан)
+        if T > 1 or past_kv is not None:
+            new_kv_cache = []
+        else:
+            new_kv_cache = None
+            
+        current_past = past_kv
+
+        for i, block in enumerate(self.blocks):
+            layer_past = current_past[i] if (current_past and i < len(current_past)) else None
+            x, layer_kv = block(x, layer_past)
+            
+            if new_kv_cache is not None:
+                new_kv_cache.append(layer_kv)
+
+        x = self.ln_f(x)
+        logits = self.lm_head(x)
+        return logits, new_kv_cache
+
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: torch.Tensor,
+        max_new_tokens: int = 100,
+        temperature: float = 0.8,
+        top_p: float = 0.95,
+        repetition_penalty: float = 1.0,
+        do_sample: bool = True,
+        eos_token_id: int = 50256
+    ) -> torch.Tensor:
+        kv_cache = [None] * NUM_LAYERS
+        current_ids = input_ids.clone()
+
+        for step in range(max_new_tokens):
+            if step == 0:
+                # Первый проход: обрабатываем весь вход
+                input_for_model = current_ids
+            else:
+                # Инкрементальные проходы: обрабатываем только последний сгенерированный токен
+                input_for_model = current_ids[:, -1].unsqueeze(-1) # 🚀 ВОССТАНОВЛЕНА ОБОРОРВАННАЯ ЧАСТЬ
+
+            logits, kv_cache = self(input_for_model, kv_cache)
+            next_token_logits = logits[:, -1, :] # Логиты только для последнего токена
+
+            # Применение температуры
+            if temperature > 0:
+                next_token_logits = next_token_logits / temperature
+
+            # Применение Repetition Penalty
+            if repetition_penalty != 1.0:
+                for i in range(current_ids.shape[0]):
+                    unique_tokens = torch.unique(current_ids[i]).tolist()
+                    for token_id in unique_tokens:
+                        score = next_token_logits[i, token_id]
+                        if score < 0:
+                            next_token_logits[i, token_id] = score * repetition_penalty
+                        else:
+                            next_token_logits[i, token_id] = score / repetition_penalty
+
+            # Top-P сэмплирование
+            if do_sample and top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
+                cumulative_probs = torch.softmax(sorted_logits, dim=-1).cumsum(dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                
+                # Сдвигаем назад, чтобы сохранить первый токен, превысивший top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                next_token_logits = next_token_logits.masked_fill(indices_to_remove, float('-inf'))
+
+            # Сэмплирование или жадный выбор
+            if do_sample and temperature > 0:
+                probs = torch.softmax(next_token_logits, dim=-1)
+                if torch.isnan(probs).any() or torch.isinf(probs).any():
+                    next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+                else:
+                    next_token = torch.multinomial(probs, num_samples=1)
+            else:
+                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+
+            if next_token.item() == eos_token_id:
+                break
+
+            current_ids = torch.cat([current_ids, next_token], dim=1)
+
+        return current_ids
+
+
+if __name__ == "__main__":
+    os.makedirs("models", exist_ok=True)
+
+    model = GPTPyTorch().to(device)
+    model.eval()
+
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Device: {device}")
+    print(f"Total parameters: {total_params / 1e6:.2f}M") # ~200.75M
+
+    # 1. Проверка первого прохода (T=50)
+    input_ids_T50 = torch.randint(0, VOCAB_SIZE, (1, 50), device=device)
+    with torch.no_grad():
+        logits_50, kv_cache_50 = model(input_ids_T50)
+        
+        # КРИТИЧЕСКАЯ ПРОВЕРКА: kv_cache_50 не должен быть None
+        assert kv_cache_50 is not None and len(kv_cache_50) == NUM_LAYERS
+        
+        expected_k_shape = (1, NUM_HEADS, 50, HEAD_DIM)
+        assert kv_cache_50[0][0].shape == expected_k_shape
+        print(f"Initial logits shape: {logits_50.shape}")
+        print(f"Initial KV-cache seqlen: {kv_cache_50[0][0].size(2)} (Head Dim: {kv_cache_50[0][0].size(3)})")
+
+    # 2. Проверка инкрементального прохода (T=1)
+    input_ids_T1 = torch.randint(0, VOCAB_SIZE, (1, 1), device=device)
+    with torch.no_grad():
+        logits_51, kv_cache_51 = model(input_ids_T1, past_kv=kv_cache_50)
+
+        # Проверка длины кеша: 50 + 1 = 51
+        assert kv_cache_51[0][0].size(2) == 51
+        print(f"Incremental logits shape: {logits_51.shape}")
+        print(f"Incremental KV-cache seqlen: {kv_cache_51[0][0].size(2)}")
+
+    # 3. Проверка функции generate
+    generated = model.generate(input_ids_T50, max_new_tokens=5, temperature=0.8, top_p=0.9)
+    print(f"Generated sequence length (50 + 5): {generated.shape[1]}")
+
+    save_path = "models/JiRack_H12_L12_V50257_D768_MSL8192_FF768x4.pt"
+    torch.save(model.state_dict(), save_path)
+    print(f"Model successfully saved to {save_path}")

source/JiRack_H12_L18_V50257_D768_MSL8192_FF768x4.py

@@ -0,0 +1,318 @@
+# Copyright (c) 2025 CMS Manhattan
+# All rights reserved.
+# Author: Konstantin Vladimirovich Grabko
+# Email: grabko@cmsmanhattan.com
+# Phone: +1(516)777-0945
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+#
+# Additional terms:
+# Any commercial use or distribution of this software or derivative works
+# requires explicit written permission from the copyright holder.
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, List
+import math
+
+# ========================================
+# Model Configuration (GPT-2 Medium Style)
+# ========================================
+VOCAB_SIZE = 50257
+MODEL_DIM = 768
+NUM_HEADS = 12
+NUM_LAYERS = 18       # Increased depth
+MAX_SEQ_LEN = 8192
+FFN_HIDDEN_DIM = 4 * MODEL_DIM
+HEAD_DIM = MODEL_DIM // NUM_HEADS
+
+# ИСПРАВЛЕНИЕ: ROCm/HIP-совместимая проверка устройства
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif hasattr(torch, 'hip') and torch.hip.is_available():
+    device = torch.device("cuda") 
+else:
+    device = torch.device("cpu")
+
+# -------------------------------
+# Learned Positional Embedding
+# -------------------------------
+class LearnedPositionalEmbedding(nn.Module):
+    def __init__(self, max_seq_len: int, embed_dim: int):
+        super().__init__()
+        self.pos_emb = nn.Parameter(torch.zeros(max_seq_len, embed_dim))
+
+    def forward(self, x: torch.Tensor, pos_offset: int = 0) -> torch.Tensor:
+        seq_len = x.size(1)
+        if pos_offset + seq_len > self.pos_emb.size(0):
+            # Проверка на выход за пределы MAX_SEQ_LEN
+            raise ValueError("Sequence length exceeds MAX_SEQ_LEN defined in position embedding.")
+        
+        pos = self.pos_emb[pos_offset : pos_offset + seq_len]
+        return x + pos.unsqueeze(0)
+
+
+# -------------------------------
+# MultiHeadAttention (MHA)
+# -------------------------------
+class MultiHeadAttention(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.q_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.k_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.v_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.out_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.scale = HEAD_DIM ** -0.5
+
+    def forward(self, x: torch.Tensor, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        B, T, D = x.shape
+        
+        q = self.q_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+
+        # 1. KV-кеш и определение смещения
+        pos_offset = 0
+        seqlen_k_new = k.size(2)
+        if past_kv is not None:
+            past_k, past_v = past_kv
+            k = torch.cat([past_k, k], dim=2)
+            v = torch.cat([past_v, v], dim=2)
+            pos_offset = past_k.size(2)
+        
+        seqlen_k = k.size(2) # Общая длина K
+        new_kv = (k, v)
+
+        # 2. Расчет внимания
+        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+
+        # 3. КРИТИЧЕСКОЕ ИСПРАВЛЕНИЕ МАСКИРОВАНИЯ (Causal Mask)
+        if T == seqlen_k_new and seqlen_k > 0:
+            # Создаем маску T x seqlen_k
+            mask = torch.full((T, seqlen_k), 
+                              float("-inf"), 
+                              device=x.device, 
+                              dtype=attn.dtype)
+            
+            # Разрешаем всем новым токенам видеть все старые токены (past_kv)
+            mask[:, :pos_offset] = 0.0
+
+            # Применяем треугольную маску для текущих T токенов
+            current_causal_mask = torch.tril(torch.ones(T, T, device=x.device, dtype=torch.bool))
+            mask[:, pos_offset : pos_offset + T].masked_fill_(~current_causal_mask, float('-inf'))
+            
+            # Применяем маску к весам внимания
+            attn = attn + mask[None, None, :, :]
+
+        # 4. Выход
+        attn = F.softmax(attn, dim=-1)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).contiguous().view(B, T, D)
+        out = self.out_proj(out)
+
+        return out, new_kv
+
+
+# -------------------------------
+# FeedForward (GELU, GPT-style)
+# -------------------------------
+class FeedForward(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.c_fc = nn.Linear(MODEL_DIM, FFN_HIDDEN_DIM, bias=False)
+        self.c_proj = nn.Linear(FFN_HIDDEN_DIM, MODEL_DIM, bias=False)
+
+    def forward(self, x):
+        return self.c_proj(F.gelu(self.c_fc(x), approximate='tanh'))
+
+
+# -------------------------------
+# Transformer Block (Post-Norm, GPT-style)
+# -------------------------------
+class TransformerBlock(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.attn = MultiHeadAttention()
+        self.ffn = FeedForward()
+        # ИСПРАВЛЕНИЕ: Добавлен стандартный eps
+        self.norm1 = nn.LayerNorm(MODEL_DIM, eps=1e-5) 
+        self.norm2 = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+
+    def forward(self, x, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        # Post-Normalization (GPT Style): Input -> Attn(Norm(Input)) + Input -> FFN(Norm(Result)) + Result
+        attn_out, new_kv = self.attn(self.norm1(x), past_kv)
+        x = x + attn_out
+        x = x + self.ffn(self.norm2(x))
+        return x, new_kv
+
+
+# -------------------------------
+# Главная модель GPTPyTorch (18 слоев)
+# -------------------------------
+class GPTPyTorch(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.token_emb = nn.Embedding(VOCAB_SIZE, MODEL_DIM)
+        self.pos_emb = LearnedPositionalEmbedding(MAX_SEQ_LEN, MODEL_DIM)
+        self.blocks = nn.ModuleList([TransformerBlock() for _ in range(NUM_LAYERS)])
+        self.ln_f = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+        self.lm_head = nn.Linear(MODEL_DIM, VOCAB_SIZE, bias=False)
+        
+        signature = "Konstantin V Gbabko .  original author © 2025"
+        bytes_tensor = torch.tensor([ord(c) for c in signature], dtype=torch.uint8)
+        self.register_buffer("konstantin_gbabko_proof_of_authorship", bytes_tensor)
+        self.register_buffer("konstantin_gbabko_birth_date", torch.tensor([20251126], dtype=torch.int64))
+
+        self.lm_head.weight = self.token_emb.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            # ИСПРАВЛЕНИЕ: Инициализация, масштабированная по глубине сети (TFixup/ReZero style)
+            std = 0.02 / math.sqrt(2 * NUM_LAYERS) if isinstance(module, nn.Linear) and module.out_features == MODEL_DIM else 0.02
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.zeros_(module.bias)
+            nn.init.ones_(module.weight)
+
+    def forward(self, input_ids, past_kv: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None):
+        B, T = input_ids.shape
+        x = self.token_emb(input_ids)
+
+        pos_offset = 0
+        if past_kv is not None and past_kv[0] is not None:
+            pos_offset = past_kv[0][0].size(2)
+            
+        x = self.pos_emb(x, pos_offset=pos_offset)
+
+        # ИСПРАВЛЕНИЕ: Инициализируем новый кеш
+        new_kv_cache = [] if past_kv is not None or T > 1 else None
+        current_past = past_kv
+
+        for i, block in enumerate(self.blocks):
+            layer_past = current_past[i] if (current_past and i < len(current_past)) else None
+            x, layer_kv = block(x, layer_past)
+            
+            if new_kv_cache is not None:
+                new_kv_cache.append(layer_kv)
+
+        x = self.ln_f(x)
+        logits = self.lm_head(x)
+        return logits, new_kv_cache
+
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: torch.Tensor,
+        max_new_tokens: int = 100,
+        temperature: float = 0.8,
+        top_p: float = 0.95,
+        repetition_penalty: float = 1.0,
+        do_sample: bool = True,
+        eos_token_id: int = 50256
+    ) -> torch.Tensor:
+        # ИСПРАВЛЕНИЕ: Инициализируем KV cache как список None кортежей
+        kv_cache = [None] * NUM_LAYERS
+        current_ids = input_ids.clone()
+
+        for step in range(max_new_tokens):
+            if step == 0:
+                input_for_model = current_ids
+            else:
+                input_for_model = current_ids[:, -1].unsqueeze(-1)
+
+            logits, kv_cache = self(input_for_model, kv_cache)
+            next_token_logits = logits[:, -1, :]
+
+            if temperature > 0:
+                next_token_logits = next_token_logits / temperature
+
+            # Repetition Penalty (логика сохранена)
+            if repetition_penalty != 1.0:
+                for i in range(current_ids.shape[0]):
+                    unique_tokens = torch.unique(current_ids[i]).tolist()
+                    for token_id in unique_tokens:
+                        score = next_token_logits[i, token_id]
+                        if score < 0:
+                            next_token_logits[i, token_id] = score * repetition_penalty
+                        else:
+                            next_token_logits[i, token_id] = score / repetition_penalty
+
+            # Top-P сэмплирование (логика сохранена)
+            if do_sample and top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
+                cumulative_probs = torch.softmax(sorted_logits, dim=-1).cumsum(dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                next_token_logits = next_token_logits.masked_fill(indices_to_remove, float('-inf'))
+
+            # Сэмплирование
+            if do_sample and temperature > 0:
+                probs = torch.softmax(next_token_logits, dim=-1)
+                if torch.isnan(probs).any() or torch.isinf(probs).any():
+                    next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+                else:
+                    next_token = torch.multinomial(probs, num_samples=1)
+            else:
+                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+
+            if next_token.item() == eos_token_id:
+                break
+
+            current_ids = torch.cat([current_ids, next_token], dim=1)
+
+        return current_ids
+
+
+if __name__ == "__main__":
+    os.makedirs("models", exist_ok=True)
+
+    model = GPTPyTorch().to(device)
+    model.eval()
+
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Device: {device}")
+    print(f"Total parameters: {total_params / 1e6:.2f}M") # ~167.33M
+
+    # 1. Проверка первого прохода (T=50)
+    input_ids_T50 = torch.randint(0, VOCAB_SIZE, (1, 50), device=device)
+    with torch.no_grad():
+        logits_50, kv_cache_50 = model(input_ids_T50)
+        expected_k_shape = (1, NUM_HEADS, 50, HEAD_DIM)
+        assert kv_cache_50[0][0].shape == expected_k_shape
+        print(f"Initial logits shape: {logits_50.shape}")
+        print(f"Initial KV-cache seqlen: {kv_cache_50[0][0].size(2)}")
+
+    # 2. Проверка инкрементального прохода (T=1)
+    input_ids_T1 = torch.randint(0, VOCAB_SIZE, (1, 1), device=device)
+    with torch.no_grad():
+        logits_51, kv_cache_51 = model(input_ids_T1, past_kv=kv_cache_50)
+
+        # Проверка длины кеша: 50 + 1 = 51
+        assert kv_cache_51[0][0].size(2) == 51
+        print(f"Incremental logits shape: {logits_51.shape}")
+        print(f"Incremental KV-cache seqlen: {kv_cache_51[0][0].size(2)}")
+
+    # 3. Проверка функции generate
+    generated = model.generate(input_ids_T50, max_new_tokens=5, temperature=0.8, top_p=0.9)
+    print(f"Generated sequence length (50 + 5): {generated.shape[1]}")
+
+    save_path = "models/JiRack_GPT_L18_PostNorm_fixed.pt"
+    torch.save(model.state_dict(), save_path)
+    print(f"Model successfully saved to {save_path}")

source/JiRack_H12_L24_V50257_D768_MSL8192_FF768x4.py

@@ -0,0 +1,317 @@
+# Copyright (c) 2025 CMS Manhattan
+# All rights reserved.
+# Author: Konstantin Vladimirovich Grabko
+# Email: grabko@cmsmanhattan.com
+# Phone: +1(516)777-0945
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+#
+# Additional terms:
+# Any commercial use or distribution of this software or derivative works
+# requires explicit written permission from the copyright holder.
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, List
+import math
+
+# ========================================
+# Model Configuration (GPT-2 Large Style)
+# ========================================
+VOCAB_SIZE = 50257
+MODEL_DIM = 768
+NUM_HEADS = 12
+NUM_LAYERS = 24       # Increased depth (GPT-2 Large equivalent)
+MAX_SEQ_LEN = 8192
+FFN_HIDDEN_DIM = 4 * MODEL_DIM
+HEAD_DIM = MODEL_DIM // NUM_HEADS
+
+# ИСПРАВЛЕНИЕ: ROCm/HIP-совместимая проверка устройства
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif hasattr(torch, 'hip') and torch.hip.is_available():
+    device = torch.device("cuda") 
+else:
+    device = torch.device("cpu")
+
+# -------------------------------
+# Learned Positional Embedding
+# -------------------------------
+class LearnedPositionalEmbedding(nn.Module):
+    def __init__(self, max_seq_len: int, embed_dim: int):
+        super().__init__()
+        self.pos_emb = nn.Parameter(torch.zeros(max_seq_len, embed_dim))
+
+    def forward(self, x: torch.Tensor, pos_offset: int = 0) -> torch.Tensor:
+        seq_len = x.size(1)
+        if pos_offset + seq_len > self.pos_emb.size(0):
+            raise ValueError("Sequence length exceeds MAX_SEQ_LEN defined in position embedding.")
+        
+        pos = self.pos_emb[pos_offset : pos_offset + seq_len]
+        return x + pos.unsqueeze(0)
+
+
+# -------------------------------
+# MultiHeadAttention (MHA)
+# -------------------------------
+class MultiHeadAttention(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.q_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.k_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.v_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.out_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.scale = HEAD_DIM ** -0.5
+
+    def forward(self, x: torch.Tensor, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        B, T, D = x.shape
+        
+        q = self.q_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+
+        # 1. KV-кеш и определение смещения
+        pos_offset = 0
+        seqlen_k_new = k.size(2)
+        if past_kv is not None:
+            past_k, past_v = past_kv
+            k = torch.cat([past_k, k], dim=2)
+            v = torch.cat([past_v, v], dim=2)
+            pos_offset = past_k.size(2)
+        
+        seqlen_k = k.size(2) # Общая длина K
+        new_kv = (k, v)
+
+        # 2. Расчет внимания
+        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+
+        # 3. КРИТИЧЕСКОЕ ИСПРАВЛЕНИЕ МАСКИРОВАНИЯ (Causal Mask)
+        if T == seqlen_k_new and seqlen_k > 0:
+            # Создаем маску T x seqlen_k
+            mask = torch.full((T, seqlen_k), 
+                              float("-inf"), 
+                              device=x.device, 
+                              dtype=attn.dtype)
+            
+            # Разрешаем всем новым токенам видеть все старые токены (past_kv)
+            mask[:, :pos_offset] = 0.0
+
+            # Применяем треугольную маску для текущих T токенов
+            current_causal_mask = torch.tril(torch.ones(T, T, device=x.device, dtype=torch.bool))
+            mask[:, pos_offset : pos_offset + T].masked_fill_(~current_causal_mask, float('-inf'))
+            
+            # Применяем маску к весам внимания
+            attn = attn + mask[None, None, :, :]
+
+        # 4. Выход
+        attn = F.softmax(attn, dim=-1)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).contiguous().view(B, T, D)
+        out = self.out_proj(out)
+
+        return out, new_kv
+
+
+# -------------------------------
+# FeedForward (GELU, GPT-style)
+# -------------------------------
+class FeedForward(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.c_fc = nn.Linear(MODEL_DIM, FFN_HIDDEN_DIM, bias=False)
+        self.c_proj = nn.Linear(FFN_HIDDEN_DIM, MODEL_DIM, bias=False)
+
+    def forward(self, x):
+        return self.c_proj(F.gelu(self.c_fc(x), approximate='tanh'))
+
+
+# -------------------------------
+# Transformer Block (Post-Norm, GPT-style)
+# -------------------------------
+class TransformerBlock(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.attn = MultiHeadAttention()
+        self.ffn = FeedForward()
+        # ИСПРАВЛЕНИЕ: Добавлен стандартный eps
+        self.norm1 = nn.LayerNorm(MODEL_DIM, eps=1e-5) 
+        self.norm2 = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+
+    def forward(self, x, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        # Post-Normalization (GPT Style)
+        attn_out, new_kv = self.attn(self.norm1(x), past_kv)
+        x = x + attn_out
+        x = x + self.ffn(self.norm2(x))
+        return x, new_kv
+
+
+# -------------------------------
+# Главная модель GPTPyTorch (24 слоя)
+# -------------------------------
+class GPTPyTorch(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.token_emb = nn.Embedding(VOCAB_SIZE, MODEL_DIM)
+        self.pos_emb = LearnedPositionalEmbedding(MAX_SEQ_LEN, MODEL_DIM)
+        self.blocks = nn.ModuleList([TransformerBlock() for _ in range(NUM_LAYERS)])
+        self.ln_f = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+        self.lm_head = nn.Linear(MODEL_DIM, VOCAB_SIZE, bias=False)
+        
+        signature = "Konstantin V Gbabko .  original author © 2025"
+        bytes_tensor = torch.tensor([ord(c) for c in signature], dtype=torch.uint8)
+        self.register_buffer("konstantin_gbabko_proof_of_authorship", bytes_tensor)
+        self.register_buffer("konstantin_gbabko_birth_date", torch.tensor([20251126], dtype=torch.int64))
+
+        self.lm_head.weight = self.token_emb.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            # ИСПРАВЛЕНИЕ: Инициализация, масштабированная по глубине сети (TFixup/ReZero style)
+            # Стандартное отклонение уменьшается с ростом числа слоев
+            std = 0.02 / math.sqrt(2 * NUM_LAYERS) if isinstance(module, nn.Linear) and module.out_features == MODEL_DIM else 0.02
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.zeros_(module.bias)
+            nn.init.ones_(module.weight)
+
+    def forward(self, input_ids, past_kv: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None):
+        B, T = input_ids.shape
+        x = self.token_emb(input_ids)
+
+        pos_offset = 0
+        if past_kv is not None and past_kv[0] is not None:
+            pos_offset = past_kv[0][0].size(2)
+            
+        x = self.pos_emb(x, pos_offset=pos_offset)
+
+        # Инициализация нового кеша
+        new_kv_cache = [] if past_kv is not None or T > 1 else None
+        current_past = past_kv
+
+        for i, block in enumerate(self.blocks):
+            layer_past = current_past[i] if (current_past and i < len(current_past)) else None
+            x, layer_kv = block(x, layer_past)
+            
+            if new_kv_cache is not None:
+                new_kv_cache.append(layer_kv)
+
+        x = self.ln_f(x)
+        logits = self.lm_head(x)
+        return logits, new_kv_cache
+
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: torch.Tensor,
+        max_new_tokens: int = 100,
+        temperature: float = 0.8,
+        top_p: float = 0.95,
+        repetition_penalty: float = 1.0,
+        do_sample: bool = True,
+        eos_token_id: int = 50256
+    ) -> torch.Tensor:
+        kv_cache = [None] * NUM_LAYERS
+        current_ids = input_ids.clone()
+
+        for step in range(max_new_tokens):
+            if step == 0:
+                input_for_model = current_ids
+            else:
+                input_for_model = current_ids[:, -1].unsqueeze(-1)
+
+            logits, kv_cache = self(input_for_model, kv_cache)
+            next_token_logits = logits[:, -1, :]
+
+            if temperature > 0:
+                next_token_logits = next_token_logits / temperature
+
+            # Repetition Penalty (логика сохранена)
+            if repetition_penalty != 1.0:
+                for i in range(current_ids.shape[0]):
+                    unique_tokens = torch.unique(current_ids[i]).tolist()
+                    for token_id in unique_tokens:
+                        score = next_token_logits[i, token_id]
+                        if score < 0:
+                            next_token_logits[i, token_id] = score * repetition_penalty
+                        else:
+                            next_token_logits[i, token_id] = score / repetition_penalty
+
+            # Top-P сэмплирование (логика сохранена)
+            if do_sample and top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
+                cumulative_probs = torch.softmax(sorted_logits, dim=-1).cumsum(dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                next_token_logits = next_token_logits.masked_fill(indices_to_remove, float('-inf'))
+
+            # Сэмплирование
+            if do_sample and temperature > 0:
+                probs = torch.softmax(next_token_logits, dim=-1)
+                if torch.isnan(probs).any() or torch.isinf(probs).any():
+                    next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+                else:
+                    next_token = torch.multinomial(probs, num_samples=1)
+            else:
+                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+
+            if next_token.item() == eos_token_id:
+                break
+
+            current_ids = torch.cat([current_ids, next_token], dim=1)
+
+        return current_ids
+
+
+if __name__ == "__main__":
+    os.makedirs("models", exist_ok=True)
+
+    model = GPTPyTorch().to(device)
+    model.eval()
+
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Device: {device}")
+    print(f"Total parameters: {total_params / 1e6:.2f}M") # ~221.75M
+
+    # 1. Проверка первого прохода (T=50)
+    input_ids_T50 = torch.randint(0, VOCAB_SIZE, (1, 50), device=device)
+    with torch.no_grad():
+        logits_50, kv_cache_50 = model(input_ids_T50)
+        expected_k_shape = (1, NUM_HEADS, 50, HEAD_DIM)
+        assert kv_cache_50[0][0].shape == expected_k_shape
+        print(f"Initial logits shape: {logits_50.shape}")
+        print(f"Initial KV-cache seqlen: {kv_cache_50[0][0].size(2)}")
+
+    # 2. Проверка инкрементального прохода (T=1)
+    input_ids_T1 = torch.randint(0, VOCAB_SIZE, (1, 1), device=device)
+    with torch.no_grad():
+        logits_51, kv_cache_51 = model(input_ids_T1, past_kv=kv_cache_50)
+
+        # Проверка длины кеша: 50 + 1 = 51
+        assert kv_cache_51[0][0].size(2) == 51
+        print(f"Incremental logits shape: {logits_51.shape}")
+        print(f"Incremental KV-cache seqlen: {kv_cache_51[0][0].size(2)}")
+
+    # 3. Проверка функции generate
+    generated = model.generate(input_ids_T50, max_new_tokens=5, temperature=0.8, top_p=0.9)
+    print(f"Generated sequence length (50 + 5): {generated.shape[1]}")
+
+    save_path = "models/JiRack_GPT_L24_PostNorm_fixed.pt"
+    torch.save(model.state_dict(), save_path)
+    print(f"Model successfully saved to {save_path}")

source/JiRack_H12_L32_V50257_D768_MSL8192_FF768x4.py

@@ -0,0 +1,317 @@
+# Copyright (c) 2025 CMS Manhattan
+# All rights reserved.
+# Author: Konstantin Vladimirovich Grabko
+# Email: grabko@cmsmanhattan.com
+# Phone: +1(516)777-0945
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+#
+# Additional terms:
+# Any commercial use or distribution of this software or derivative works
+# requires explicit written permission from the copyright holder.
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, List
+import math
+
+# ========================================
+# Model Configuration (GPT-2 XL Style)
+# ========================================
+VOCAB_SIZE = 50257
+MODEL_DIM = 768
+NUM_HEADS = 12
+NUM_LAYERS = 32       # Increased depth (GPT-2 XL equivalent)
+MAX_SEQ_LEN = 8192
+FFN_HIDDEN_DIM = 4 * MODEL_DIM
+HEAD_DIM = MODEL_DIM // NUM_HEADS
+
+# ИСПРАВЛЕНИЕ: ROCm/HIP-совместимая проверка устройства
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif hasattr(torch, 'hip') and torch.hip.is_available():
+    device = torch.device("cuda") 
+else:
+    device = torch.device("cpu")
+
+# -------------------------------
+# Learned Positional Embedding
+# -------------------------------
+class LearnedPositionalEmbedding(nn.Module):
+    def __init__(self, max_seq_len: int, embed_dim: int):
+        super().__init__()
+        self.pos_emb = nn.Parameter(torch.zeros(max_seq_len, embed_dim))
+
+    def forward(self, x: torch.Tensor, pos_offset: int = 0) -> torch.Tensor:
+        seq_len = x.size(1)
+        if pos_offset + seq_len > self.pos_emb.size(0):
+            raise ValueError("Sequence length exceeds MAX_SEQ_LEN defined in position embedding.")
+        
+        pos = self.pos_emb[pos_offset : pos_offset + seq_len]
+        return x + pos.unsqueeze(0)
+
+
+# -------------------------------
+# MultiHeadAttention (MHA)
+# -------------------------------
+class MultiHeadAttention(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.q_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.k_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.v_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.out_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.scale = HEAD_DIM ** -0.5
+
+    def forward(self, x: torch.Tensor, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        B, T, D = x.shape
+        
+        q = self.q_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+
+        # 1. KV-кеш и определение смещения
+        pos_offset = 0
+        seqlen_k_new = k.size(2)
+        if past_kv is not None:
+            past_k, past_v = past_kv
+            k = torch.cat([past_k, k], dim=2)
+            v = torch.cat([past_v, v], dim=2)
+            pos_offset = past_k.size(2)
+        
+        seqlen_k = k.size(2) # Общая длина K
+        new_kv = (k, v)
+
+        # 2. Расчет внимания
+        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+
+        # 3. КРИТИЧЕСКОЕ ИСПРАВЛЕНИЕ МАСКИРОВАНИЯ (Causal Mask)
+        if T == seqlen_k_new and seqlen_k > 0:
+            # Создаем маску T x seqlen_k
+            mask = torch.full((T, seqlen_k), 
+                              float("-inf"), 
+                              device=x.device, 
+                              dtype=attn.dtype)
+            
+            # Разрешаем всем новым токенам видеть все старые токены (past_kv)
+            mask[:, :pos_offset] = 0.0
+
+            # Применяем треугольную маску для текущих T токенов
+            current_causal_mask = torch.tril(torch.ones(T, T, device=x.device, dtype=torch.bool))
+            mask[:, pos_offset : pos_offset + T].masked_fill_(~current_causal_mask, float('-inf'))
+            
+            # Применяем маску к весам внимания
+            attn = attn + mask[None, None, :, :]
+
+        # 4. Выход
+        attn = F.softmax(attn, dim=-1)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).contiguous().view(B, T, D)
+        out = self.out_proj(out)
+
+        return out, new_kv
+
+
+# -------------------------------
+# FeedForward (GELU, GPT-style)
+# -------------------------------
+class FeedForward(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.c_fc = nn.Linear(MODEL_DIM, FFN_HIDDEN_DIM, bias=False)
+        self.c_proj = nn.Linear(FFN_HIDDEN_DIM, MODEL_DIM, bias=False)
+
+    def forward(self, x):
+        return self.c_proj(F.gelu(self.c_fc(x), approximate='tanh'))
+
+
+# -------------------------------
+# Transformer Block (Post-Norm, GPT-style)
+# -------------------------------
+class TransformerBlock(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.attn = MultiHeadAttention()
+        self.ffn = FeedForward()
+        # ИСПРАВЛЕНИЕ: Добавлен стандартный eps
+        self.norm1 = nn.LayerNorm(MODEL_DIM, eps=1e-5) 
+        self.norm2 = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+
+    def forward(self, x, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        # Post-Normalization (GPT Style)
+        attn_out, new_kv = self.attn(self.norm1(x), past_kv)
+        x = x + attn_out
+        x = x + self.ffn(self.norm2(x))
+        return x, new_kv
+
+
+# -------------------------------
+# Главная модель GPTPyTorch (32 слоя)
+# -------------------------------
+class GPTPyTorch(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.token_emb = nn.Embedding(VOCAB_SIZE, MODEL_DIM)
+        self.pos_emb = LearnedPositionalEmbedding(MAX_SEQ_LEN, MODEL_DIM)
+        self.blocks = nn.ModuleList([TransformerBlock() for _ in range(NUM_LAYERS)])
+        self.ln_f = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+        self.lm_head = nn.Linear(MODEL_DIM, VOCAB_SIZE, bias=False)
+        
+        signature = "Konstantin V Gbabko .  original author © 2025"
+        bytes_tensor = torch.tensor([ord(c) for c in signature], dtype=torch.uint8)
+        self.register_buffer("konstantin_gbabko_proof_of_authorship", bytes_tensor)
+        self.register_buffer("konstantin_gbabko_birth_date", torch.tensor([20251126], dtype=torch.int64))
+
+        self.lm_head.weight = self.token_emb.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            # ИСПРАВЛЕНИЕ: Инициализация, масштабированная по глубине сети (TFixup/ReZero style). 
+            # Critical for NUM_LAYERS = 32
+            std = 0.02 / math.sqrt(2 * NUM_LAYERS) if isinstance(module, nn.Linear) and module.out_features == MODEL_DIM else 0.02
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.zeros_(module.bias)
+            nn.init.ones_(module.weight)
+
+    def forward(self, input_ids, past_kv: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None):
+        B, T = input_ids.shape
+        x = self.token_emb(input_ids)
+
+        pos_offset = 0
+        if past_kv is not None and past_kv[0] is not None:
+            pos_offset = past_kv[0][0].size(2)
+            
+        x = self.pos_emb(x, pos_offset=pos_offset)
+
+        # Инициализация нового кеша
+        new_kv_cache = [] if past_kv is not None or T > 1 else None
+        current_past = past_kv
+
+        for i, block in enumerate(self.blocks):
+            layer_past = current_past[i] if (current_past and i < len(current_past)) else None
+            x, layer_kv = block(x, layer_past)
+            
+            if new_kv_cache is not None:
+                new_kv_cache.append(layer_kv)
+
+        x = self.ln_f(x)
+        logits = self.lm_head(x)
+        return logits, new_kv_cache
+
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: torch.Tensor,
+        max_new_tokens: int = 100,
+        temperature: float = 0.8,
+        top_p: float = 0.95,
+        repetition_penalty: float = 1.0,
+        do_sample: bool = True,
+        eos_token_id: int = 50256
+    ) -> torch.Tensor:
+        kv_cache = [None] * NUM_LAYERS
+        current_ids = input_ids.clone()
+
+        for step in range(max_new_tokens):
+            if step == 0:
+                input_for_model = current_ids
+            else:
+                input_for_model = current_ids[:, -1].unsqueeze(-1)
+
+            logits, kv_cache = self(input_for_model, kv_cache)
+            next_token_logits = logits[:, -1, :]
+
+            if temperature > 0:
+                next_token_logits = next_token_logits / temperature
+
+            # Repetition Penalty (логика сохранена)
+            if repetition_penalty != 1.0:
+                for i in range(current_ids.shape[0]):
+                    unique_tokens = torch.unique(current_ids[i]).tolist()
+                    for token_id in unique_tokens:
+                        score = next_token_logits[i, token_id]
+                        if score < 0:
+                            next_token_logits[i, token_id] = score * repetition_penalty
+                        else:
+                            next_token_logits[i, token_id] = score / repetition_penalty
+
+            # Top-P сэмплирование (логика сохранена)
+            if do_sample and top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
+                cumulative_probs = torch.softmax(sorted_logits, dim=-1).cumsum(dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                next_token_logits = next_token_logits.masked_fill(indices_to_remove, float('-inf'))
+
+            # Сэмплирование
+            if do_sample and temperature > 0:
+                probs = torch.softmax(next_token_logits, dim=-1)
+                if torch.isnan(probs).any() or torch.isinf(probs).any():
+                    next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+                else:
+                    next_token = torch.multinomial(probs, num_samples=1)
+            else:
+                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+
+            if next_token.item() == eos_token_id:
+                break
+
+            current_ids = torch.cat([current_ids, next_token], dim=1)
+
+        return current_ids
+
+
+if __name__ == "__main__":
+    os.makedirs("models", exist_ok=True)
+
+    model = GPTPyTorch().to(device)
+    model.eval()
+
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Device: {device}")
+    print(f"Total parameters: {total_params / 1e6:.2f}M") # ~295.1M
+
+    # 1. Проверка первого прохода (T=50)
+    input_ids_T50 = torch.randint(0, VOCAB_SIZE, (1, 50), device=device)
+    with torch.no_grad():
+        logits_50, kv_cache_50 = model(input_ids_T50)
+        expected_k_shape = (1, NUM_HEADS, 50, HEAD_DIM)
+        assert kv_cache_50[0][0].shape == expected_k_shape
+        print(f"Initial logits shape: {logits_50.shape}")
+        print(f"Initial KV-cache seqlen: {kv_cache_50[0][0].size(2)}")
+
+    # 2. Проверка инкрементального прохода (T=1)
+    input_ids_T1 = torch.randint(0, VOCAB_SIZE, (1, 1), device=device)
+    with torch.no_grad():
+        logits_51, kv_cache_51 = model(input_ids_T1, past_kv=kv_cache_50)
+
+        # Проверка длины кеша: 50 + 1 = 51
+        assert kv_cache_51[0][0].size(2) == 51
+        print(f"Incremental logits shape: {logits_51.shape}")
+        print(f"Incremental KV-cache seqlen: {kv_cache_51[0][0].size(2)}")
+
+    # 3. Проверка функции generate
+    generated = model.generate(input_ids_T50, max_new_tokens=5, temperature=0.8, top_p=0.9)
+    print(f"Generated sequence length (50 + 5): {generated.shape[1]}")
+
+    save_path = "models/JiRack_GPT_L32_PostNorm_fixed.pt"
+    torch.save(model.state_dict(), save_path)
+    print(f"Model successfully saved to {save_path}")

source/JiRack_H12_L6_V50257_D768_MSL8192_FF768x4.py

@@ -0,0 +1,316 @@
+# Copyright (c) 2025 CMS Manhattan
+# All rights reserved.
+# Author: Konstantin Vladimirovich Grabko
+# Email: grabko@cmsmanhattan.com
+# Phone: +1(516)777-0945
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+#
+# Additional terms:
+# Any commercial use or distribution of this software or derivative works
+# requires explicit written permission from the copyright holder.
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, List
+import math
+
+# ========================================
+# Model Configuration (GPT-2 Small Style)
+# ========================================
+VOCAB_SIZE = 50257
+MODEL_DIM = 768
+NUM_HEADS = 12
+NUM_LAYERS = 6        # Back to 6 layers
+MAX_SEQ_LEN = 8192
+FFN_HIDDEN_DIM = 4 * MODEL_DIM
+HEAD_DIM = MODEL_DIM // NUM_HEADS
+
+# ИСПРАВЛЕНИЕ: ROCm/HIP-совместимая проверка устройства
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif hasattr(torch, 'hip') and torch.hip.is_available():
+    device = torch.device("cuda") 
+else:
+    device = torch.device("cpu")
+
+# -------------------------------
+# Learned Positional Embedding
+# -------------------------------
+class LearnedPositionalEmbedding(nn.Module):
+    def __init__(self, max_seq_len: int, embed_dim: int):
+        super().__init__()
+        self.pos_emb = nn.Parameter(torch.zeros(max_seq_len, embed_dim))
+
+    def forward(self, x: torch.Tensor, pos_offset: int = 0) -> torch.Tensor:
+        seq_len = x.size(1)
+        if pos_offset + seq_len > self.pos_emb.size(0):
+            raise ValueError("Sequence length exceeds MAX_SEQ_LEN defined in position embedding.")
+        
+        pos = self.pos_emb[pos_offset : pos_offset + seq_len]
+        return x + pos.unsqueeze(0)
+
+
+# -------------------------------
+# MultiHeadAttention (MHA)
+# -------------------------------
+class MultiHeadAttention(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.q_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.k_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.v_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.out_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.scale = HEAD_DIM ** -0.5
+
+    def forward(self, x: torch.Tensor, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        B, T, D = x.shape
+        
+        q = self.q_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+
+        # 1. KV-кеш и определение смещения
+        pos_offset = 0
+        seqlen_k_new = k.size(2)
+        if past_kv is not None:
+            past_k, past_v = past_kv
+            k = torch.cat([past_k, k], dim=2)
+            v = torch.cat([past_v, v], dim=2)
+            pos_offset = past_k.size(2)
+        
+        seqlen_k = k.size(2) # Общая длина K
+        new_kv = (k, v)
+
+        # 2. Расчет внимания
+        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+
+        # 3. КРИТИЧЕСКОЕ ИСПРАВЛЕНИЕ МАСКИРОВАНИЯ (Causal Mask)
+        if T == seqlen_k_new and seqlen_k > 0:
+            # Создаем маску T x seqlen_k
+            mask = torch.full((T, seqlen_k), 
+                              float("-inf"), 
+                              device=x.device, 
+                              dtype=attn.dtype)
+            
+            # Разрешаем всем новым токенам видеть все старые токены (past_kv)
+            mask[:, :pos_offset] = 0.0
+
+            # Применяем треугольную маску для текущих T токенов
+            current_causal_mask = torch.tril(torch.ones(T, T, device=x.device, dtype=torch.bool))
+            mask[:, pos_offset : pos_offset + T].masked_fill_(~current_causal_mask, float('-inf'))
+            
+            # Применяем маску к весам внимания
+            attn = attn + mask[None, None, :, :]
+
+        # 4. Выход
+        attn = F.softmax(attn, dim=-1)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).contiguous().view(B, T, D)
+        out = self.out_proj(out)
+
+        return out, new_kv
+
+
+# -------------------------------
+# FeedForward (GELU, GPT-style)
+# -------------------------------
+class FeedForward(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.c_fc = nn.Linear(MODEL_DIM, FFN_HIDDEN_DIM, bias=False)
+        self.c_proj = nn.Linear(FFN_HIDDEN_DIM, MODEL_DIM, bias=False)
+
+    def forward(self, x):
+        return self.c_proj(F.gelu(self.c_fc(x), approximate='tanh'))
+
+
+# -------------------------------
+# Transformer Block (Post-Norm, GPT-style)
+# -------------------------------
+class TransformerBlock(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.attn = MultiHeadAttention()
+        self.ffn = FeedForward()
+        # ИСПРАВЛЕНИЕ: Добавлен стандартный eps
+        self.norm1 = nn.LayerNorm(MODEL_DIM, eps=1e-5) 
+        self.norm2 = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+
+    def forward(self, x, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        # Post-Normalization (GPT Style)
+        attn_out, new_kv = self.attn(self.norm1(x), past_kv)
+        x = x + attn_out
+        x = x + self.ffn(self.norm2(x))
+        return x, new_kv
+
+
+# -------------------------------
+# Главная модель GPTPyTorch (6 слоев)
+# -------------------------------
+class GPTPyTorch(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.token_emb = nn.Embedding(VOCAB_SIZE, MODEL_DIM)
+        self.pos_emb = LearnedPositionalEmbedding(MAX_SEQ_LEN, MODEL_DIM)
+        self.blocks = nn.ModuleList([TransformerBlock() for _ in range(NUM_LAYERS)])
+        self.ln_f = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+        self.lm_head = nn.Linear(MODEL_DIM, VOCAB_SIZE, bias=False)
+        
+        signature = "Konstantin V Gbabko .  original author © 2025"
+        bytes_tensor = torch.tensor([ord(c) for c in signature], dtype=torch.uint8)
+        self.register_buffer("konstantin_gbabko_proof_of_authorship", bytes_tensor)
+        self.register_buffer("konstantin_gbabko_birth_date", torch.tensor([20251126], dtype=torch.int64))
+
+        self.lm_head.weight = self.token_emb.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            # ИСПРАВЛЕНИЕ: Инициализация, масштабированная по глубине сети
+            std = 0.02 / math.sqrt(2 * NUM_LAYERS) if isinstance(module, nn.Linear) and module.out_features == MODEL_DIM else 0.02
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.zeros_(module.bias)
+            nn.init.ones_(module.weight)
+
+    def forward(self, input_ids, past_kv: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None):
+        B, T = input_ids.shape
+        x = self.token_emb(input_ids)
+
+        pos_offset = 0
+        if past_kv is not None and past_kv[0] is not None:
+            pos_offset = past_kv[0][0].size(2)
+            
+        x = self.pos_emb(x, pos_offset=pos_offset)
+
+        # Инициализация нового кеша
+        new_kv_cache = [] if past_kv is not None or T > 1 else None
+        current_past = past_kv
+
+        for i, block in enumerate(self.blocks):
+            layer_past = current_past[i] if (current_past and i < len(current_past)) else None
+            x, layer_kv = block(x, layer_past)
+            
+            if new_kv_cache is not None:
+                new_kv_cache.append(layer_kv)
+
+        x = self.ln_f(x)
+        logits = self.lm_head(x)
+        return logits, new_kv_cache
+
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: torch.Tensor,
+        max_new_tokens: int = 100,
+        temperature: float = 0.8,
+        top_p: float = 0.95,
+        repetition_penalty: float = 1.0,
+        do_sample: bool = True,
+        eos_token_id: int = 50256
+    ) -> torch.Tensor:
+        kv_cache = [None] * NUM_LAYERS
+        current_ids = input_ids.clone()
+
+        for step in range(max_new_tokens):
+            if step == 0:
+                input_for_model = current_ids
+            else:
+                input_for_model = current_ids[:, -1].unsqueeze(-1)
+
+            logits, kv_cache = self(input_for_model, kv_cache)
+            next_token_logits = logits[:, -1, :]
+
+            if temperature > 0:
+                next_token_logits = next_token_logits / temperature
+
+            # Repetition Penalty (логика сохранена)
+            if repetition_penalty != 1.0:
+                for i in range(current_ids.shape[0]):
+                    unique_tokens = torch.unique(current_ids[i]).tolist()
+                    for token_id in unique_tokens:
+                        score = next_token_logits[i, token_id]
+                        if score < 0:
+                            next_token_logits[i, token_id] = score * repetition_penalty
+                        else:
+                            next_token_logits[i, token_id] = score / repetition_penalty
+
+            # Top-P сэмплирование (логика сохранена)
+            if do_sample and top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
+                cumulative_probs = torch.softmax(sorted_logits, dim=-1).cumsum(dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                next_token_logits = next_token_logits.masked_fill(indices_to_remove, float('-inf'))
+
+            # Сэмплирование
+            if do_sample and temperature > 0:
+                probs = torch.softmax(next_token_logits, dim=-1)
+                if torch.isnan(probs).any() or torch.isinf(probs).any():
+                    next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+                else:
+                    next_token = torch.multinomial(probs, num_samples=1)
+            else:
+                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+
+            if next_token.item() == eos_token_id:
+                break
+
+            current_ids = torch.cat([current_ids, next_token], dim=1)
+
+        return current_ids
+
+
+if __name__ == "__main__":
+    os.makedirs("models", exist_ok=True)
+
+    model = GPTPyTorch().to(device)
+    model.eval()
+
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Device: {device}")
+    print(f"Total parameters: {total_params / 1e6:.2f}M") # ~124.44M
+
+    # 1. Проверка первого прохода (T=50)
+    input_ids_T50 = torch.randint(0, VOCAB_SIZE, (1, 50), device=device)
+    with torch.no_grad():
+        logits_50, kv_cache_50 = model(input_ids_T50)
+        expected_k_shape = (1, NUM_HEADS, 50, HEAD_DIM)
+        assert kv_cache_50[0][0].shape == expected_k_shape
+        print(f"Initial logits shape: {logits_50.shape}")
+        print(f"Initial KV-cache seqlen: {kv_cache_50[0][0].size(2)}")
+
+    # 2. Проверка инкрементального прохода (T=1)
+    input_ids_T1 = torch.randint(0, VOCAB_SIZE, (1, 1), device=device)
+    with torch.no_grad():
+        logits_51, kv_cache_51 = model(input_ids_T1, past_kv=kv_cache_50)
+
+        # Проверка длины кеша: 50 + 1 = 51
+        assert kv_cache_51[0][0].size(2) == 51
+        print(f"Incremental logits shape: {logits_51.shape}")
+        print(f"Incremental KV-cache seqlen: {kv_cache_51[0][0].size(2)}")
+
+    # 3. Проверка функции generate
+    generated = model.generate(input_ids_T50, max_new_tokens=5, temperature=0.8, top_p=0.9)
+    print(f"Generated sequence length (50 + 5): {generated.shape[1]}")
+
+    save_path = "models/JiRack_GPT_L6_PostNorm_fixed.pt"
+    torch.save(model.state_dict(), save_path)
+    print(f"Model successfully saved to {save_path}")

source/JiRack_H16_L24_V50257_D768_MSL8192_FF768x4.py

@@ -0,0 +1,318 @@
+# Copyright (c) 2025 CMS Manhattan
+# All rights reserved.
+# Author: Konstantin Vladimirovich Grabko
+# Email: grabko@cmsmanhattan.com
+# Phone: +1(516)777-0945
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+#
+# Additional terms:
+# Any commercial use or distribution of this software or derivative works
+# requires explicit written permission from the copyright holder.
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, List
+import math
+
+# ========================================
+# Model Configuration (L=24, H=16)
+# ========================================
+VOCAB_SIZE = 50257
+MODEL_DIM = 768
+NUM_HEADS = 16        # Changed to 16
+NUM_LAYERS = 24       # Changed to 24
+MAX_SEQ_LEN = 8192
+FFN_HIDDEN_DIM = 4 * MODEL_DIM
+HEAD_DIM = MODEL_DIM // NUM_HEADS # Recalculated: 768 / 16 = 48
+
+# ИСПРАВЛЕНИЕ: ROCm/HIP-совместимая проверка устройства
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif hasattr(torch, 'hip') and torch.hip.is_available():
+    device = torch.device("cuda") 
+else:
+    device = torch.device("cpu")
+
+# -------------------------------
+# Learned Positional Embedding
+# -------------------------------
+class LearnedPositionalEmbedding(nn.Module):
+    def __init__(self, max_seq_len: int, embed_dim: int):
+        super().__init__()
+        self.pos_emb = nn.Parameter(torch.zeros(max_seq_len, embed_dim))
+
+    def forward(self, x: torch.Tensor, pos_offset: int = 0) -> torch.Tensor:
+        seq_len = x.size(1)
+        if pos_offset + seq_len > self.pos_emb.size(0):
+            raise ValueError("Sequence length exceeds MAX_SEQ_LEN defined in position embedding.")
+        
+        pos = self.pos_emb[pos_offset : pos_offset + seq_len]
+        return x + pos.unsqueeze(0)
+
+
+# -------------------------------
+# MultiHeadAttention (MHA)
+# -------------------------------
+class MultiHeadAttention(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.q_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.k_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.v_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.out_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.scale = HEAD_DIM ** -0.5
+
+    def forward(self, x: torch.Tensor, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        B, T, D = x.shape
+        
+        # NOTE: D is MODEL_DIM
+        q = self.q_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+
+        # 1. KV-кеш и определение смещения
+        pos_offset = 0
+        seqlen_k_new = k.size(2)
+        if past_kv is not None:
+            past_k, past_v = past_kv
+            k = torch.cat([past_k, k], dim=2)
+            v = torch.cat([past_v, v], dim=2)
+            pos_offset = past_k.size(2)
+        
+        seqlen_k = k.size(2) # Общая длина K
+        new_kv = (k, v)
+
+        # 2. Расчет внимания
+        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+
+        # 3. КРИТИЧЕСКОЕ ИСПРАВЛЕНИЕ МАСКИРОВАНИЯ (Causal Mask)
+        if T == seqlen_k_new and seqlen_k > 0:
+            # Создаем маску T (query length) x seqlen_k (key length)
+            mask = torch.full((T, seqlen_k), 
+                              float("-inf"), 
+                              device=x.device, 
+                              dtype=attn.dtype)
+            
+            # Разрешаем всем новым токенам видеть все старые токены (past_kv)
+            mask[:, :pos_offset] = 0.0
+
+            # Применяем треугольную маску для текущих T токенов
+            current_causal_mask = torch.tril(torch.ones(T, T, device=x.device, dtype=torch.bool))
+            mask[:, pos_offset : pos_offset + T].masked_fill_(~current_causal_mask, float('-inf'))
+            
+            # Применяем маску к весам внимания
+            attn = attn + mask[None, None, :, :]
+
+        # 4. Выход
+        attn = F.softmax(attn, dim=-1)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).contiguous().view(B, T, D)
+        out = self.out_proj(out)
+
+        return out, new_kv
+
+
+# -------------------------------
+# FeedForward (GELU, GPT-style)
+# -------------------------------
+class FeedForward(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.c_fc = nn.Linear(MODEL_DIM, FFN_HIDDEN_DIM, bias=False)
+        self.c_proj = nn.Linear(FFN_HIDDEN_DIM, MODEL_DIM, bias=False)
+
+    def forward(self, x):
+        return self.c_proj(F.gelu(self.c_fc(x), approximate='tanh'))
+
+
+# -------------------------------
+# Transformer Block (Post-Norm, GPT-style)
+# -------------------------------
+class TransformerBlock(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.attn = MultiHeadAttention()
+        self.ffn = FeedForward()
+        # ИСПРАВЛЕНИЕ: Добавлен стандартный eps
+        self.norm1 = nn.LayerNorm(MODEL_DIM, eps=1e-5) 
+        self.norm2 = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+
+    def forward(self, x, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        # Post-Normalization (GPT Style)
+        attn_out, new_kv = self.attn(self.norm1(x), past_kv)
+        x = x + attn_out
+        x = x + self.ffn(self.norm2(x))
+        return x, new_kv
+
+
+# -------------------------------
+# Главная модель GPTPyTorch (L=24, H=16)
+# -------------------------------
+class GPTPyTorch(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.token_emb = nn.Embedding(VOCAB_SIZE, MODEL_DIM)
+        self.pos_emb = LearnedPositionalEmbedding(MAX_SEQ_LEN, MODEL_DIM)
+        self.blocks = nn.ModuleList([TransformerBlock() for _ in range(NUM_LAYERS)])
+        self.ln_f = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+        self.lm_head = nn.Linear(MODEL_DIM, VOCAB_SIZE, bias=False)
+        
+        signature = "Konstantin V Gbabko .  original author © 2025"
+        bytes_tensor = torch.tensor([ord(c) for c in signature], dtype=torch.uint8)
+        self.register_buffer("konstantin_gbabko_proof_of_authorship", bytes_tensor)
+        self.register_buffer("konstantin_gbabko_birth_date", torch.tensor([20251126], dtype=torch.int64))
+
+        self.lm_head.weight = self.token_emb.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            # ИСПРАВЛЕНИЕ: Инициализация, масштабированная по глубине сети
+            std = 0.02 / math.sqrt(2 * NUM_LAYERS) if isinstance(module, nn.Linear) and module.out_features == MODEL_DIM else 0.02
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.zeros_(module.bias)
+            nn.init.ones_(module.weight)
+
+    def forward(self, input_ids, past_kv: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None):
+        B, T = input_ids.shape
+        x = self.token_emb(input_ids)
+
+        pos_offset = 0
+        if past_kv is not None and past_kv[0] is not None:
+            pos_offset = past_kv[0][0].size(2)
+            
+        x = self.pos_emb(x, pos_offset=pos_offset)
+
+        # Инициализация нового кеша
+        new_kv_cache = [] if past_kv is not None or T > 1 else None
+        current_past = past_kv
+
+        for i, block in enumerate(self.blocks):
+            layer_past = current_past[i] if (current_past and i < len(current_past)) else None
+            x, layer_kv = block(x, layer_past)
+            
+            if new_kv_cache is not None:
+                new_kv_cache.append(layer_kv)
+
+        x = self.ln_f(x)
+        logits = self.lm_head(x)
+        return logits, new_kv_cache
+
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: torch.Tensor,
+        max_new_tokens: int = 100,
+        temperature: float = 0.8,
+        top_p: float = 0.95,
+        repetition_penalty: float = 1.0,
+        do_sample: bool = True,
+        eos_token_id: int = 50256
+    ) -> torch.Tensor:
+        kv_cache = [None] * NUM_LAYERS
+        current_ids = input_ids.clone()
+
+        for step in range(max_new_tokens):
+            if step == 0:
+                input_for_model = current_ids
+            else:
+                input_for_model = current_ids[:, -1].unsqueeze(-1)
+
+            logits, kv_cache = self(input_for_model, kv_cache)
+            next_token_logits = logits[:, -1, :]
+
+            if temperature > 0:
+                next_token_logits = next_token_logits / temperature
+
+            # Repetition Penalty (логика сохранена)
+            if repetition_penalty != 1.0:
+                for i in range(current_ids.shape[0]):
+                    unique_tokens = torch.unique(current_ids[i]).tolist()
+                    for token_id in unique_tokens:
+                        score = next_token_logits[i, token_id]
+                        if score < 0:
+                            next_token_logits[i, token_id] = score * repetition_penalty
+                        else:
+                            next_token_logits[i, token_id] = score / repetition_penalty
+
+            # Top-P сэмплирование (логика сохранена)
+            if do_sample and top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
+                cumulative_probs = torch.softmax(sorted_logits, dim=-1).cumsum(dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                next_token_logits = next_token_logits.masked_fill(indices_to_remove, float('-inf'))
+
+            # Сэмплирование
+            if do_sample and temperature > 0:
+                probs = torch.softmax(next_token_logits, dim=-1)
+                if torch.isnan(probs).any() or torch.isinf(probs).any():
+                    next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+                else:
+                    next_token = torch.multinomial(probs, num_samples=1)
+            else:
+                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+
+            if next_token.item() == eos_token_id:
+                break
+
+            current_ids = torch.cat([current_ids, next_token], dim=1)
+
+        return current_ids
+
+
+if __name__ == "__main__":
+    os.makedirs("models", exist_ok=True)
+
+    model = GPTPyTorch().to(device)
+    model.eval()
+
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Device: {device}")
+    print(f"Total parameters: {total_params / 1e6:.2f}M") # ~221.75M
+
+    # 1. Проверка первого прохода (T=50)
+    input_ids_T50 = torch.randint(0, VOCAB_SIZE, (1, 50), device=device)
+    with torch.no_grad():
+        logits_50, kv_cache_50 = model(input_ids_T50)
+        expected_k_shape = (1, NUM_HEADS, 50, HEAD_DIM)
+        # Проверяем, что размерность головы HEAD_DIM (48) соблюдена
+        assert kv_cache_50[0][0].shape == expected_k_shape
+        print(f"Initial logits shape: {logits_50.shape}")
+        print(f"Initial KV-cache seqlen: {kv_cache_50[0][0].size(2)} (Head Dim: {kv_cache_50[0][0].size(3)})")
+
+    # 2. Проверка инкрементального прохода (T=1)
+    input_ids_T1 = torch.randint(0, VOCAB_SIZE, (1, 1), device=device)
+    with torch.no_grad():
+        logits_51, kv_cache_51 = model(input_ids_T1, past_kv=kv_cache_50)
+
+        # Проверка длины кеша: 50 + 1 = 51
+        assert kv_cache_51[0][0].size(2) == 51
+        print(f"Incremental logits shape: {logits_51.shape}")
+        print(f"Incremental KV-cache seqlen: {kv_cache_51[0][0].size(2)}")
+
+    # 3. Проверка функции generate
+    generated = model.generate(input_ids_T50, max_new_tokens=5, temperature=0.8, top_p=0.9)
+    print(f"Generated sequence length (50 + 5): {generated.shape[1]}")
+
+    save_path = "models/JiRack_GPT_L24_H16_PostNorm_fixed.pt"
+    torch.save(model.state_dict(), save_path)
+    print(f"Model successfully saved to {save_path}")

source/JiRack_H16_L32_V50257_D768_MSL8192_FF768x4.py

@@ -0,0 +1,318 @@
+# Copyright (c) 2025 CMS Manhattan
+# All rights reserved.
+# Author: Konstantin Vladimirovich Grabko
+# Email: grabko@cmsmanhattan.com
+# Phone: +1(516)777-0945
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+#
+# Additional terms:
+# Any commercial use or distribution of this software or derivative works
+# requires explicit written permission from the copyright holder.
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, List
+import math
+
+# ========================================
+# Model Configuration (L=32, H=16, D=768)
+# ========================================
+VOCAB_SIZE = 50257
+MODEL_DIM = 768
+NUM_HEADS = 16
+NUM_LAYERS = 32       # Deepest version yet
+MAX_SEQ_LEN = 8192
+FFN_HIDDEN_DIM = 4 * MODEL_DIM
+HEAD_DIM = MODEL_DIM // NUM_HEADS # 768 / 16 = 48
+
+# ИСПРАВЛЕНИЕ: ROCm/HIP-совместимая проверка устройства
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif hasattr(torch, 'hip') and torch.hip.is_available():
+    device = torch.device("cuda") 
+else:
+    device = torch.device("cpu")
+
+# -------------------------------
+# Learned Positional Embedding
+# -------------------------------
+class LearnedPositionalEmbedding(nn.Module):
+    def __init__(self, max_seq_len: int, embed_dim: int):
+        super().__init__()
+        self.pos_emb = nn.Parameter(torch.zeros(max_seq_len, embed_dim))
+
+    def forward(self, x: torch.Tensor, pos_offset: int = 0) -> torch.Tensor:
+        seq_len = x.size(1)
+        if pos_offset + seq_len > self.pos_emb.size(0):
+            raise ValueError("Sequence length exceeds MAX_SEQ_LEN defined in position embedding.")
+        
+        pos = self.pos_emb[pos_offset : pos_offset + seq_len]
+        return x + pos.unsqueeze(0)
+
+
+# -------------------------------
+# MultiHeadAttention (MHA)
+# -------------------------------
+class MultiHeadAttention(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.q_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.k_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.v_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.out_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.scale = HEAD_DIM ** -0.5
+
+    def forward(self, x: torch.Tensor, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        B, T, D = x.shape
+        
+        q = self.q_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+
+        # 1. KV-кеш и определение смещения
+        pos_offset = 0
+        seqlen_k_new = k.size(2)
+        if past_kv is not None:
+            past_k, past_v = past_kv
+            k = torch.cat([past_k, k], dim=2)
+            v = torch.cat([past_v, v], dim=2)
+            pos_offset = past_k.size(2)
+        
+        seqlen_k = k.size(2) # Общая длина K
+        new_kv = (k, v)
+
+        # 2. Расчет внимания
+        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+
+        # 3. КРИТИЧЕСКОЕ ИСПРАВЛЕНИЕ МАСКИРОВАНИЯ (Causal Mask)
+        if T == seqlen_k_new and seqlen_k > 0:
+            # Создаем маску T (query length) x seqlen_k (key length)
+            mask = torch.full((T, seqlen_k), 
+                              float("-inf"), 
+                              device=x.device, 
+                              dtype=attn.dtype)
+            
+            # Разрешаем всем новым токенам видеть все старые токены (past_kv)
+            mask[:, :pos_offset] = 0.0
+
+            # Применяем треугольную маску для текущих T токенов
+            current_causal_mask = torch.tril(torch.ones(T, T, device=x.device, dtype=torch.bool))
+            mask[:, pos_offset : pos_offset + T].masked_fill_(~current_causal_mask, float('-inf'))
+            
+            # Применяем маску к весам внимания
+            attn = attn + mask[None, None, :, :]
+
+        # 4. Выход
+        attn = F.softmax(attn, dim=-1)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).contiguous().view(B, T, D)
+        out = self.out_proj(out)
+
+        return out, new_kv
+
+
+# -------------------------------
+# FeedForward (GELU, GPT-style)
+# -------------------------------
+class FeedForward(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.c_fc = nn.Linear(MODEL_DIM, FFN_HIDDEN_DIM, bias=False)
+        self.c_proj = nn.Linear(FFN_HIDDEN_DIM, MODEL_DIM, bias=False)
+
+    def forward(self, x):
+        return self.c_proj(F.gelu(self.c_fc(x), approximate='tanh'))
+
+
+# -------------------------------
+# Transformer Block (Post-Norm, GPT-style)
+# -------------------------------
+class TransformerBlock(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.attn = MultiHeadAttention()
+        self.ffn = FeedForward()
+        # ИСПРАВЛЕНИЕ: Добавлен стандартный eps
+        self.norm1 = nn.LayerNorm(MODEL_DIM, eps=1e-5) 
+        self.norm2 = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+
+    def forward(self, x, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        # Post-Normalization (GPT Style)
+        attn_out, new_kv = self.attn(self.norm1(x), past_kv)
+        x = x + attn_out
+        x = x + self.ffn(self.norm2(x))
+        return x, new_kv
+
+
+# -------------------------------
+# Главная модель GPTPyTorch (L=32, H=16)
+# -------------------------------
+class GPTPyTorch(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.token_emb = nn.Embedding(VOCAB_SIZE, MODEL_DIM)
+        self.pos_emb = LearnedPositionalEmbedding(MAX_SEQ_LEN, MODEL_DIM)
+        self.blocks = nn.ModuleList([TransformerBlock() for _ in range(NUM_LAYERS)])
+        self.ln_f = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+        self.lm_head = nn.Linear(MODEL_DIM, VOCAB_SIZE, bias=False)
+        
+        signature = "Konstantin V Gbabko .  original author © 2025"
+        bytes_tensor = torch.tensor([ord(c) for c in signature], dtype=torch.uint8)
+        self.register_buffer("konstantin_gbabko_proof_of_authorship", bytes_tensor)
+        self.register_buffer("konstantin_gbabko_birth_date", torch.tensor([20251126], dtype=torch.int64))
+
+        self.lm_head.weight = self.token_emb.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            # ИСПРАВЛЕНИЕ: Инициализация, масштабированная по глубине сети.
+            # Critical for NUM_LAYERS = 32
+            std = 0.02 / math.sqrt(2 * NUM_LAYERS) if isinstance(module, nn.Linear) and module.out_features == MODEL_DIM else 0.02
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.zeros_(module.bias)
+            nn.init.ones_(module.weight)
+
+    def forward(self, input_ids, past_kv: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None):
+        B, T = input_ids.shape
+        x = self.token_emb(input_ids)
+
+        pos_offset = 0
+        if past_kv is not None and past_kv[0] is not None:
+            pos_offset = past_kv[0][0].size(2)
+            
+        x = self.pos_emb(x, pos_offset=pos_offset)
+
+        # Инициализация нового кеша
+        new_kv_cache = [] if past_kv is not None or T > 1 else None
+        current_past = past_kv
+
+        for i, block in enumerate(self.blocks):
+            layer_past = current_past[i] if (current_past and i < len(current_past)) else None
+            x, layer_kv = block(x, layer_past)
+            
+            if new_kv_cache is not None:
+                new_kv_cache.append(layer_kv)
+
+        x = self.ln_f(x)
+        logits = self.lm_head(x)
+        return logits, new_kv_cache
+
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: torch.Tensor,
+        max_new_tokens: int = 100,
+        temperature: float = 0.8,
+        top_p: float = 0.95,
+        repetition_penalty: float = 1.0,
+        do_sample: bool = True,
+        eos_token_id: int = 50256
+    ) -> torch.Tensor:
+        kv_cache = [None] * NUM_LAYERS
+        current_ids = input_ids.clone()
+
+        for step in range(max_new_tokens):
+            if step == 0:
+                input_for_model = current_ids
+            else:
+                input_for_model = current_ids[:, -1].unsqueeze(-1)
+
+            logits, kv_cache = self(input_for_model, kv_cache)
+            next_token_logits = logits[:, -1, :]
+
+            if temperature > 0:
+                next_token_logits = next_token_logits / temperature
+
+            # Repetition Penalty (логика сохранена)
+            if repetition_penalty != 1.0:
+                for i in range(current_ids.shape[0]):
+                    unique_tokens = torch.unique(current_ids[i]).tolist()
+                    for token_id in unique_tokens:
+                        score = next_token_logits[i, token_id]
+                        if score < 0:
+                            next_token_logits[i, token_id] = score * repetition_penalty
+                        else:
+                            next_token_logits[i, token_id] = score / repetition_penalty
+
+            # Top-P сэмплирование (логика сохранена)
+            if do_sample and top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
+                cumulative_probs = torch.softmax(sorted_logits, dim=-1).cumsum(dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                next_token_logits = next_token_logits.masked_fill(indices_to_remove, float('-inf'))
+
+            # Сэмплирование
+            if do_sample and temperature > 0:
+                probs = torch.softmax(next_token_logits, dim=-1)
+                if torch.isnan(probs).any() or torch.isinf(probs).any():
+                    next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+                else:
+                    next_token = torch.multinomial(probs, num_samples=1)
+            else:
+                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+
+            if next_token.item() == eos_token_id:
+                break
+
+            current_ids = torch.cat([current_ids, next_token], dim=1)
+
+        return current_ids
+
+
+if __name__ == "__main__":
+    os.makedirs("models", exist_ok=True)
+
+    model = GPTPyTorch().to(device)
+    model.eval()
+
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Device: {device}")
+    print(f"Total parameters: {total_params / 1e6:.2f}M") # ~295.1M
+
+    # 1. Проверка первого прохода (T=50)
+    input_ids_T50 = torch.randint(0, VOCAB_SIZE, (1, 50), device=device)
+    with torch.no_grad():
+        logits_50, kv_cache_50 = model(input_ids_T50)
+        expected_k_shape = (1, NUM_HEADS, 50, HEAD_DIM)
+        # Проверяем, что размерность головы HEAD_DIM (48) соблюдена
+        assert kv_cache_50[0][0].shape == expected_k_shape
+        print(f"Initial logits shape: {logits_50.shape}")
+        print(f"Initial KV-cache seqlen: {kv_cache_50[0][0].size(2)} (Head Dim: {kv_cache_50[0][0].size(3)})")
+
+    # 2. Проверка инкрементального прохода (T=1)
+    input_ids_T1 = torch.randint(0, VOCAB_SIZE, (1, 1), device=device)
+    with torch.no_grad():
+        logits_51, kv_cache_51 = model(input_ids_T1, past_kv=kv_cache_50)
+
+        # Проверка длины кеша: 50 + 1 = 51
+        assert kv_cache_51[0][0].size(2) == 51
+        print(f"Incremental logits shape: {logits_51.shape}")
+        print(f"Incremental KV-cache seqlen: {kv_cache_51[0][0].size(2)}")
+
+    # 3. Проверка функции generate
+    generated = model.generate(input_ids_T50, max_new_tokens=5, temperature=0.8, top_p=0.9)
+    print(f"Generated sequence length (50 + 5): {generated.shape[1]}")
+
+    save_path = "models/JiRack_GPT_L32_H16_PostNorm_fixed.pt"
+    torch.save(model.state_dict(), save_path)
+    print(f"Model successfully saved to {save_path}")

source/JiRack_H6_L6_V50257_D768_MSL8192_FF768x4.py

@@ -0,0 +1,317 @@
+# Copyright (c) 2025 CMS Manhattan
+# All rights reserved.
+# Author: Konstantin Vladimirovich Grabko
+# Email: grabko@cmsmanhattan.com
+# Phone: +1(516)777-0945
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+#
+# Additional terms:
+# Any commercial use or distribution of this software or derivative works
+# requires explicit written permission from the copyright holder.
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, List
+import math
+
+# ========================================
+# Model Configuration (L=6, H=6, D=768)
+# ========================================
+VOCAB_SIZE = 50257
+MODEL_DIM = 768
+NUM_HEADS = 6         # Changed to 6
+NUM_LAYERS = 6        # Set to 6 layers
+MAX_SEQ_LEN = 8192
+FFN_HIDDEN_DIM = 4 * MODEL_DIM
+HEAD_DIM = MODEL_DIM // NUM_HEADS # 768 / 6 = 128
+
+# ИСПРАВЛЕНИЕ: ROCm/HIP-совместимая проверка устройства
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif hasattr(torch, 'hip') and torch.hip.is_available():
+    device = torch.device("cuda") 
+else:
+    device = torch.device("cpu")
+
+# -------------------------------
+# Learned Positional Embedding
+# -------------------------------
+class LearnedPositionalEmbedding(nn.Module):
+    def __init__(self, max_seq_len: int, embed_dim: int):
+        super().__init__()
+        self.pos_emb = nn.Parameter(torch.zeros(max_seq_len, embed_dim))
+
+    def forward(self, x: torch.Tensor, pos_offset: int = 0) -> torch.Tensor:
+        seq_len = x.size(1)
+        if pos_offset + seq_len > self.pos_emb.size(0):
+            raise ValueError("Sequence length exceeds MAX_SEQ_LEN defined in position embedding.")
+        
+        pos = self.pos_emb[pos_offset : pos_offset + seq_len]
+        return x + pos.unsqueeze(0)
+
+
+# -------------------------------
+# MultiHeadAttention (MHA)
+# -------------------------------
+class MultiHeadAttention(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.q_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.k_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.v_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.out_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.scale = HEAD_DIM ** -0.5
+
+    def forward(self, x: torch.Tensor, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        B, T, D = x.shape
+        
+        q = self.q_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+
+        # 1. KV-кеш и определение смещения
+        pos_offset = 0
+        seqlen_k_new = k.size(2)
+        if past_kv is not None:
+            past_k, past_v = past_kv
+            k = torch.cat([past_k, k], dim=2)
+            v = torch.cat([past_v, v], dim=2)
+            pos_offset = past_k.size(2)
+        
+        seqlen_k = k.size(2) # Общая длина K
+        new_kv = (k, v)
+
+        # 2. Расчет внимания
+        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+
+        # 3. КРИТИЧЕСКОЕ ИСПРАВЛЕНИЕ МАСКИРОВАНИЯ (Causal Mask)
+        if T == seqlen_k_new and seqlen_k > 0:
+            # Создаем маску T (query length) x seqlen_k (key length)
+            mask = torch.full((T, seqlen_k), 
+                              float("-inf"), 
+                              device=x.device, 
+                              dtype=attn.dtype)
+            
+            # Разрешаем всем новым токенам видеть все старые токены (past_kv)
+            mask[:, :pos_offset] = 0.0
+
+            # Применяем треугольную маску для текущих T токенов
+            current_causal_mask = torch.tril(torch.ones(T, T, device=x.device, dtype=torch.bool))
+            mask[:, pos_offset : pos_offset + T].masked_fill_(~current_causal_mask, float('-inf'))
+            
+            # Применяем маску к весам внимания
+            attn = attn + mask[None, None, :, :]
+
+        # 4. Выход
+        attn = F.softmax(attn, dim=-1)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).contiguous().view(B, T, D)
+        out = self.out_proj(out)
+
+        return out, new_kv
+
+
+# -------------------------------
+# FeedForward (GELU, GPT-style)
+# -------------------------------
+class FeedForward(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.c_fc = nn.Linear(MODEL_DIM, FFN_HIDDEN_DIM, bias=False)
+        self.c_proj = nn.Linear(FFN_HIDDEN_DIM, MODEL_DIM, bias=False)
+
+    def forward(self, x):
+        return self.c_proj(F.gelu(self.c_fc(x), approximate='tanh'))
+
+
+# -------------------------------
+# Transformer Block (Post-Norm, GPT-style)
+# -------------------------------
+class TransformerBlock(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.attn = MultiHeadAttention()
+        self.ffn = FeedForward()
+        # ИСПРАВЛЕНИЕ: Добавлен стандартный eps
+        self.norm1 = nn.LayerNorm(MODEL_DIM, eps=1e-5) 
+        self.norm2 = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+
+    def forward(self, x, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        # Post-Normalization (GPT Style)
+        attn_out, new_kv = self.attn(self.norm1(x), past_kv)
+        x = x + attn_out
+        x = x + self.ffn(self.norm2(x))
+        return x, new_kv
+
+
+# -------------------------------
+# Главная модель GPTPyTorch (L=6, H=6)
+# -------------------------------
+class GPTPyTorch(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.token_emb = nn.Embedding(VOCAB_SIZE, MODEL_DIM)
+        self.pos_emb = LearnedPositionalEmbedding(MAX_SEQ_LEN, MODEL_DIM)
+        self.blocks = nn.ModuleList([TransformerBlock() for _ in range(NUM_LAYERS)])
+        self.ln_f = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+        self.lm_head = nn.Linear(MODEL_DIM, VOCAB_SIZE, bias=False)
+        
+        signature = "Konstantin V Gbabko .  original author © 2025"
+        bytes_tensor = torch.tensor([ord(c) for c in signature], dtype=torch.uint8)
+        self.register_buffer("konstantin_gbabko_proof_of_authorship", bytes_tensor)
+        self.register_buffer("konstantin_gbabko_birth_date", torch.tensor([20251126], dtype=torch.int64))
+
+        self.lm_head.weight = self.token_emb.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            # ИСПРАВЛЕНИЕ: Инициализация, масштабированная по глубине сети.
+            std = 0.02 / math.sqrt(2 * NUM_LAYERS) if isinstance(module, nn.Linear) and module.out_features == MODEL_DIM else 0.02
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.zeros_(module.bias)
+            nn.init.ones_(module.weight)
+
+    def forward(self, input_ids, past_kv: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None):
+        B, T = input_ids.shape
+        x = self.token_emb(input_ids)
+
+        pos_offset = 0
+        if past_kv is not None and past_kv[0] is not None:
+            pos_offset = past_kv[0][0].size(2)
+            
+        x = self.pos_emb(x, pos_offset=pos_offset)
+
+        # Инициализация нового кеша
+        new_kv_cache = [] if past_kv is not None or T > 1 else None
+        current_past = past_kv
+
+        for i, block in enumerate(self.blocks):
+            layer_past = current_past[i] if (current_past and i < len(current_past)) else None
+            x, layer_kv = block(x, layer_past)
+            
+            if new_kv_cache is not None:
+                new_kv_cache.append(layer_kv)
+
+        x = self.ln_f(x)
+        logits = self.lm_head(x)
+        return logits, new_kv_cache
+
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: torch.Tensor,
+        max_new_tokens: int = 100,
+        temperature: float = 0.8,
+        top_p: float = 0.95,
+        repetition_penalty: float = 1.0,
+        do_sample: bool = True,
+        eos_token_id: int = 50256
+    ) -> torch.Tensor:
+        kv_cache = [None] * NUM_LAYERS
+        current_ids = input_ids.clone()
+
+        for step in range(max_new_tokens):
+            if step == 0:
+                input_for_model = current_ids
+            else:
+                input_for_model = current_ids[:, -1].unsqueeze(-1)
+
+            logits, kv_cache = self(input_for_model, kv_cache)
+            next_token_logits = logits[:, -1, :]
+
+            if temperature > 0:
+                next_token_logits = next_token_logits / temperature
+
+            # Repetition Penalty (логика сохранена)
+            if repetition_penalty != 1.0:
+                for i in range(current_ids.shape[0]):
+                    unique_tokens = torch.unique(current_ids[i]).tolist()
+                    for token_id in unique_tokens:
+                        score = next_token_logits[i, token_id]
+                        if score < 0:
+                            next_token_logits[i, token_id] = score * repetition_penalty
+                        else:
+                            next_token_logits[i, token_id] = score / repetition_penalty
+
+            # Top-P сэмплирование (логика сохранена)
+            if do_sample and top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
+                cumulative_probs = torch.softmax(sorted_logits, dim=-1).cumsum(dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                next_token_logits = next_token_logits.masked_fill(indices_to_remove, float('-inf'))
+
+            # Сэмплирование
+            if do_sample and temperature > 0:
+                probs = torch.softmax(next_token_logits, dim=-1)
+                if torch.isnan(probs).any() or torch.isinf(probs).any():
+                    next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+                else:
+                    next_token = torch.multinomial(probs, num_samples=1)
+            else:
+                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+
+            if next_token.item() == eos_token_id:
+                break
+
+            current_ids = torch.cat([current_ids, next_token], dim=1)
+
+        return current_ids
+
+
+if __name__ == "__main__":
+    os.makedirs("models", exist_ok=True)
+
+    model = GPTPyTorch().to(device)
+    model.eval()
+
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Device: {device}")
+    print(f"Total parameters: {total_params / 1e6:.2f}M") # ~124.44M
+
+    # 1. Проверка первого прохода (T=50)
+    input_ids_T50 = torch.randint(0, VOCAB_SIZE, (1, 50), device=device)
+    with torch.no_grad():
+        logits_50, kv_cache_50 = model(input_ids_T50)
+        expected_k_shape = (1, NUM_HEADS, 50, HEAD_DIM)
+        # Проверяем, что размерность головы HEAD_DIM (128) соблюдена
+        assert kv_cache_50[0][0].shape == expected_k_shape
+        print(f"Initial logits shape: {logits_50.shape}")
+        print(f"Initial KV-cache seqlen: {kv_cache_50[0][0].size(2)} (Head Dim: {kv_cache_50[0][0].size(3)})")
+
+    # 2. Проверка инкрементального прохода (T=1)
+    input_ids_T1 = torch.randint(0, VOCAB_SIZE, (1, 1), device=device)
+    with torch.no_grad():
+        logits_51, kv_cache_51 = model(input_ids_T1, past_kv=kv_cache_50)
+
+        # Проверка длины кеша: 50 + 1 = 51
+        assert kv_cache_51[0][0].size(2) == 51
+        print(f"Incremental logits shape: {logits_51.shape}")
+        print(f"Incremental KV-cache seqlen: {kv_cache_51[0][0].size(2)}")
+
+    # 3. Проверка функции generate
+    generated = model.generate(input_ids_T50, max_new_tokens=5, temperature=0.8, top_p=0.9)
+    print(f"Generated sequence length (50 + 5): {generated.shape[1]}")
+
+    save_path = "models/JiRack_GPT_L6_H6_PostNorm_fixed.pt"
+    torch.save(model.state_dict(), save_path)
+    print(f"Model successfully saved to {save_path}")

source/JiRack_H8_L6_V50257_D768_MSL8192_FF768x4.py

@@ -0,0 +1,317 @@
+# Copyright (c) 2025 CMS Manhattan
+# All rights reserved.
+# Author: Konstantin Vladimirovich Grabko
+# Email: grabko@cmsmanhattan.com
+# Phone: +1(516)777-0945
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+#
+# Additional terms:
+# Any commercial use or distribution of this software or derivative works
+# requires explicit written permission from the copyright holder.
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, List
+import math
+
+# ========================================
+# Model Configuration (L=6, H=8, D=768)
+# ========================================
+VOCAB_SIZE = 50257
+MODEL_DIM = 768
+NUM_HEADS = 8         # Set to 8
+NUM_LAYERS = 6
+MAX_SEQ_LEN = 8192
+FFN_HIDDEN_DIM = 4 * MODEL_DIM
+HEAD_DIM = MODEL_DIM // NUM_HEADS # Recalculated: 768 / 8 = 96
+
+# ИСПРАВЛЕНИЕ: ROCm/HIP-совместимая проверка устройства
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif hasattr(torch, 'hip') and torch.hip.is_available():
+    device = torch.device("cuda") 
+else:
+    device = torch.device("cpu")
+
+# -------------------------------
+# Learned Positional Embedding
+# -------------------------------
+class LearnedPositionalEmbedding(nn.Module):
+    def __init__(self, max_seq_len: int, embed_dim: int):
+        super().__init__()
+        self.pos_emb = nn.Parameter(torch.zeros(max_seq_len, embed_dim))
+
+    def forward(self, x: torch.Tensor, pos_offset: int = 0) -> torch.Tensor:
+        seq_len = x.size(1)
+        if pos_offset + seq_len > self.pos_emb.size(0):
+            raise ValueError("Sequence length exceeds MAX_SEQ_LEN defined in position embedding.")
+        
+        pos = self.pos_emb[pos_offset : pos_offset + seq_len]
+        return x + pos.unsqueeze(0)
+
+
+# -------------------------------
+# MultiHeadAttention (MHA)
+# -------------------------------
+class MultiHeadAttention(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.q_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.k_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.v_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.out_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.scale = HEAD_DIM ** -0.5
+
+    def forward(self, x: torch.Tensor, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        B, T, D = x.shape
+        
+        q = self.q_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+
+        # 1. KV-кеш и определение смещения
+        pos_offset = 0
+        seqlen_k_new = k.size(2)
+        if past_kv is not None:
+            past_k, past_v = past_kv
+            k = torch.cat([past_k, k], dim=2)
+            v = torch.cat([past_v, v], dim=2)
+            pos_offset = past_k.size(2)
+        
+        seqlen_k = k.size(2) # Общая длина K
+        new_kv = (k, v)
+
+        # 2. Расчет внимания
+        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+
+        # 3. КРИТИЧЕСКОЕ ИСПРАВЛЕНИЕ МАСКИРОВАНИЯ (Causal Mask)
+        if T == seqlen_k_new and seqlen_k > 0:
+            # Создаем маску T (query length) x seqlen_k (key length)
+            mask = torch.full((T, seqlen_k), 
+                              float("-inf"), 
+                              device=x.device, 
+                              dtype=attn.dtype)
+            
+            # Разрешаем всем новым токенам видеть все старые токены (past_kv)
+            mask[:, :pos_offset] = 0.0
+
+            # Применяем треугольную маску для текущих T токенов
+            current_causal_mask = torch.tril(torch.ones(T, T, device=x.device, dtype=torch.bool))
+            mask[:, pos_offset : pos_offset + T].masked_fill_(~current_causal_mask, float('-inf'))
+            
+            # Применяем маску к весам внимания
+            attn = attn + mask[None, None, :, :]
+
+        # 4. Выход
+        attn = F.softmax(attn, dim=-1)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).contiguous().view(B, T, D)
+        out = self.out_proj(out)
+
+        return out, new_kv
+
+
+# -------------------------------
+# FeedForward (GELU, GPT-style)
+# -------------------------------
+class FeedForward(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.c_fc = nn.Linear(MODEL_DIM, FFN_HIDDEN_DIM, bias=False)
+        self.c_proj = nn.Linear(FFN_HIDDEN_DIM, MODEL_DIM, bias=False)
+
+    def forward(self, x):
+        return self.c_proj(F.gelu(self.c_fc(x), approximate='tanh'))
+
+
+# -------------------------------
+# Transformer Block (Post-Norm, GPT-style)
+# -------------------------------
+class TransformerBlock(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.attn = MultiHeadAttention()
+        self.ffn = FeedForward()
+        # ИСПРАВЛЕНИЕ: Добавлен стандартный eps
+        self.norm1 = nn.LayerNorm(MODEL_DIM, eps=1e-5) 
+        self.norm2 = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+
+    def forward(self, x, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        # Post-Normalization (GPT Style)
+        attn_out, new_kv = self.attn(self.norm1(x), past_kv)
+        x = x + attn_out
+        x = x + self.ffn(self.norm2(x))
+        return x, new_kv
+
+
+# -------------------------------
+# Главная модель GPTPyTorch (L=6, H=8)
+# -------------------------------
+class GPTPyTorch(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.token_emb = nn.Embedding(VOCAB_SIZE, MODEL_DIM)
+        self.pos_emb = LearnedPositionalEmbedding(MAX_SEQ_LEN, MODEL_DIM)
+        self.blocks = nn.ModuleList([TransformerBlock() for _ in range(NUM_LAYERS)])
+        self.ln_f = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+        self.lm_head = nn.Linear(MODEL_DIM, VOCAB_SIZE, bias=False)
+        
+        signature = "Konstantin V Gbabko .  original author © 2025"
+        bytes_tensor = torch.tensor([ord(c) for c in signature], dtype=torch.uint8)
+        self.register_buffer("konstantin_gbabko_proof_of_authorship", bytes_tensor)
+        self.register_buffer("konstantin_gbabko_birth_date", torch.tensor([20251126], dtype=torch.int64))
+
+        self.lm_head.weight = self.token_emb.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            # ИСПРАВЛЕНИЕ: Инициализация, масштабированная по глубине сети.
+            std = 0.02 / math.sqrt(2 * NUM_LAYERS) if isinstance(module, nn.Linear) and module.out_features == MODEL_DIM else 0.02
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.zeros_(module.bias)
+            nn.init.ones_(module.weight)
+
+    def forward(self, input_ids, past_kv: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None):
+        B, T = input_ids.shape
+        x = self.token_emb(input_ids)
+
+        pos_offset = 0
+        if past_kv is not None and past_kv[0] is not None:
+            pos_offset = past_kv[0][0].size(2)
+            
+        x = self.pos_emb(x, pos_offset=pos_offset)
+
+        # Инициализация нового кеша
+        new_kv_cache = [] if past_kv is not None or T > 1 else None
+        current_past = past_kv
+
+        for i, block in enumerate(self.blocks):
+            layer_past = current_past[i] if (current_past and i < len(current_past)) else None
+            x, layer_kv = block(x, layer_past)
+            
+            if new_kv_cache is not None:
+                new_kv_cache.append(layer_kv)
+
+        x = self.ln_f(x)
+        logits = self.lm_head(x)
+        return logits, new_kv_cache
+
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: torch.Tensor,
+        max_new_tokens: int = 100,
+        temperature: float = 0.8,
+        top_p: float = 0.95,
+        repetition_penalty: float = 1.0,
+        do_sample: bool = True,
+        eos_token_id: int = 50256
+    ) -> torch.Tensor:
+        kv_cache = [None] * NUM_LAYERS
+        current_ids = input_ids.clone()
+
+        for step in range(max_new_tokens):
+            if step == 0:
+                input_for_model = current_ids
+            else:
+                input_for_model = current_ids[:, -1].unsqueeze(-1)
+
+            logits, kv_cache = self(input_for_model, kv_cache)
+            next_token_logits = logits[:, -1, :]
+
+            if temperature > 0:
+                next_token_logits = next_token_logits / temperature
+
+            # Repetition Penalty (логика сохранена)
+            if repetition_penalty != 1.0:
+                for i in range(current_ids.shape[0]):
+                    unique_tokens = torch.unique(current_ids[i]).tolist()
+                    for token_id in unique_tokens:
+                        score = next_token_logits[i, token_id]
+                        if score < 0:
+                            next_token_logits[i, token_id] = score * repetition_penalty
+                        else:
+                            next_token_logits[i, token_id] = score / repetition_penalty
+
+            # Top-P сэмплирование (логика сохранена)
+            if do_sample and top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
+                cumulative_probs = torch.softmax(sorted_logits, dim=-1).cumsum(dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                next_token_logits = next_token_logits.masked_fill(indices_to_remove, float('-inf'))
+
+            # Сэмплирование
+            if do_sample and temperature > 0:
+                probs = torch.softmax(next_token_logits, dim=-1)
+                if torch.isnan(probs).any() or torch.isinf(probs).any():
+                    next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+                else:
+                    next_token = torch.multinomial(probs, num_samples=1)
+            else:
+                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+
+            if next_token.item() == eos_token_id:
+                break
+
+            current_ids = torch.cat([current_ids, next_token], dim=1)
+
+        return current_ids
+
+
+if __name__ == "__main__":
+    os.makedirs("models", exist_ok=True)
+
+    model = GPTPyTorch().to(device)
+    model.eval()
+
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Device: {device}")
+    print(f"Total parameters: {total_params / 1e6:.2f}M") # ~124.44M
+
+    # 1. Проверка первого прохода (T=50)
+    input_ids_T50 = torch.randint(0, VOCAB_SIZE, (1, 50), device=device)
+    with torch.no_grad():
+        logits_50, kv_cache_50 = model(input_ids_T50)
+        expected_k_shape = (1, NUM_HEADS, 50, HEAD_DIM)
+        # Проверяем, что размерность головы HEAD_DIM (96) соблюдена
+        assert kv_cache_50[0][0].shape == expected_k_shape
+        print(f"Initial logits shape: {logits_50.shape}")
+        print(f"Initial KV-cache seqlen: {kv_cache_50[0][0].size(2)} (Head Dim: {kv_cache_50[0][0].size(3)})")
+
+    # 2. Проверка инкрементального прохода (T=1)
+    input_ids_T1 = torch.randint(0, VOCAB_SIZE, (1, 1), device=device)
+    with torch.no_grad():
+        logits_51, kv_cache_51 = model(input_ids_T1, past_kv=kv_cache_50)
+
+        # Проверка длины кеша: 50 + 1 = 51
+        assert kv_cache_51[0][0].size(2) == 51
+        print(f"Incremental logits shape: {logits_51.shape}")
+        print(f"Incremental KV-cache seqlen: {kv_cache_51[0][0].size(2)}")
+
+    # 3. Проверка функции generate
+    generated = model.generate(input_ids_T50, max_new_tokens=5, temperature=0.8, top_p=0.9)
+    print(f"Generated sequence length (50 + 5): {generated.shape[1]}")
+
+    save_path = "models/JiRack_GPT_L6_H8_PostNorm_fixed.pt"
+    torch.save(model.state_dict(), save_path)
+    print(f"Model successfully saved to {save_path}")

source/JiRack_H8_L8_V50257_D768_MSL8192_FF768x4.py

@@ -0,0 +1,317 @@
+# Copyright (c) 2025 CMS Manhattan
+# All rights reserved.
+# Author: Konstantin Vladimirovich Grabko
+# Email: grabko@cmsmanhattan.com
+# Phone: +1(516)777-0945
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, version 3 of the License.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <https://www.gnu.org/licenses/>.
+#
+# Additional terms:
+# Any commercial use or distribution of this software or derivative works
+# requires explicit written permission from the copyright holder.
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, List
+import math
+
+# ========================================
+# Model Configuration (L=8, H=8, D=768)
+# ========================================
+VOCAB_SIZE = 50257
+MODEL_DIM = 768
+NUM_HEADS = 8
+NUM_LAYERS = 8        # Set to 8 layers
+MAX_SEQ_LEN = 8192
+FFN_HIDDEN_DIM = 4 * MODEL_DIM
+HEAD_DIM = MODEL_DIM // NUM_HEADS # 768 / 8 = 96
+
+# ИСПРАВЛЕНИЕ: ROCm/HIP-совместимая проверка устройства
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif hasattr(torch, 'hip') and torch.hip.is_available():
+    device = torch.device("cuda") 
+else:
+    device = torch.device("cpu")
+
+# -------------------------------
+# Learned Positional Embedding
+# -------------------------------
+class LearnedPositionalEmbedding(nn.Module):
+    def __init__(self, max_seq_len: int, embed_dim: int):
+        super().__init__()
+        self.pos_emb = nn.Parameter(torch.zeros(max_seq_len, embed_dim))
+
+    def forward(self, x: torch.Tensor, pos_offset: int = 0) -> torch.Tensor:
+        seq_len = x.size(1)
+        if pos_offset + seq_len > self.pos_emb.size(0):
+            raise ValueError("Sequence length exceeds MAX_SEQ_LEN defined in position embedding.")
+        
+        pos = self.pos_emb[pos_offset : pos_offset + seq_len]
+        return x + pos.unsqueeze(0)
+
+
+# -------------------------------
+# MultiHeadAttention (MHA)
+# -------------------------------
+class MultiHeadAttention(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.q_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.k_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.v_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.out_proj = nn.Linear(MODEL_DIM, MODEL_DIM, bias=False)
+        self.scale = HEAD_DIM ** -0.5
+
+    def forward(self, x: torch.Tensor, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        B, T, D = x.shape
+        
+        q = self.q_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        k = self.k_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+        v = self.v_proj(x).view(B, T, NUM_HEADS, HEAD_DIM).transpose(1, 2)
+
+        # 1. KV-кеш и определение смещения
+        pos_offset = 0
+        seqlen_k_new = k.size(2)
+        if past_kv is not None:
+            past_k, past_v = past_kv
+            k = torch.cat([past_k, k], dim=2)
+            v = torch.cat([past_v, v], dim=2)
+            pos_offset = past_k.size(2)
+        
+        seqlen_k = k.size(2) # Общая длина K
+        new_kv = (k, v)
+
+        # 2. Расчет внимания
+        attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+
+        # 3. КРИТИЧЕСКОЕ ИСПРАВЛЕНИЕ МАСКИРОВАНИЯ (Causal Mask)
+        if T == seqlen_k_new and seqlen_k > 0:
+            # Создаем маску T (query length) x seqlen_k (key length)
+            mask = torch.full((T, seqlen_k), 
+                              float("-inf"), 
+                              device=x.device, 
+                              dtype=attn.dtype)
+            
+            # Разрешаем всем новым токенам видеть все старые токены (past_kv)
+            mask[:, :pos_offset] = 0.0
+
+            # Применяем треугольную маску для текущих T токенов
+            current_causal_mask = torch.tril(torch.ones(T, T, device=x.device, dtype=torch.bool))
+            mask[:, pos_offset : pos_offset + T].masked_fill_(~current_causal_mask, float('-inf'))
+            
+            # Применяем маску к весам внимания
+            attn = attn + mask[None, None, :, :]
+
+        # 4. Выход
+        attn = F.softmax(attn, dim=-1)
+        out = torch.matmul(attn, v)
+        out = out.transpose(1, 2).contiguous().view(B, T, D)
+        out = self.out_proj(out)
+
+        return out, new_kv
+
+
+# -------------------------------
+# FeedForward (GELU, GPT-style)
+# -------------------------------
+class FeedForward(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.c_fc = nn.Linear(MODEL_DIM, FFN_HIDDEN_DIM, bias=False)
+        self.c_proj = nn.Linear(FFN_HIDDEN_DIM, MODEL_DIM, bias=False)
+
+    def forward(self, x):
+        return self.c_proj(F.gelu(self.c_fc(x), approximate='tanh'))
+
+
+# -------------------------------
+# Transformer Block (Post-Norm, GPT-style)
+# -------------------------------
+class TransformerBlock(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.attn = MultiHeadAttention()
+        self.ffn = FeedForward()
+        # ИСПРАВЛЕНИЕ: Добавлен стандартный eps
+        self.norm1 = nn.LayerNorm(MODEL_DIM, eps=1e-5) 
+        self.norm2 = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+
+    def forward(self, x, past_kv: Optional[Tuple[torch.Tensor, torch.Tensor]] = None):
+        # Post-Normalization (GPT Style)
+        attn_out, new_kv = self.attn(self.norm1(x), past_kv)
+        x = x + attn_out
+        x = x + self.ffn(self.norm2(x))
+        return x, new_kv
+
+
+# -------------------------------
+# Главная модель GPTPyTorch (L=8, H=8)
+# -------------------------------
+class GPTPyTorch(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.token_emb = nn.Embedding(VOCAB_SIZE, MODEL_DIM)
+        self.pos_emb = LearnedPositionalEmbedding(MAX_SEQ_LEN, MODEL_DIM)
+        self.blocks = nn.ModuleList([TransformerBlock() for _ in range(NUM_LAYERS)])
+        self.ln_f = nn.LayerNorm(MODEL_DIM, eps=1e-5)
+        self.lm_head = nn.Linear(MODEL_DIM, VOCAB_SIZE, bias=False)
+        
+        signature = "Konstantin V Gbabko .  original author © 2025"
+        bytes_tensor = torch.tensor([ord(c) for c in signature], dtype=torch.uint8)
+        self.register_buffer("konstantin_gbabko_proof_of_authorship", bytes_tensor)
+        self.register_buffer("konstantin_gbabko_birth_date", torch.tensor([20251126], dtype=torch.int64))
+
+        self.lm_head.weight = self.token_emb.weight
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            # ИСПРАВЛЕНИЕ: Инициализация, масштабированная по глубине сети.
+            std = 0.02 / math.sqrt(2 * NUM_LAYERS) if isinstance(module, nn.Linear) and module.out_features == MODEL_DIM else 0.02
+            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+        elif isinstance(module, nn.LayerNorm):
+            nn.init.zeros_(module.bias)
+            nn.init.ones_(module.weight)
+
+    def forward(self, input_ids, past_kv: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None):
+        B, T = input_ids.shape
+        x = self.token_emb(input_ids)
+
+        pos_offset = 0
+        if past_kv is not None and past_kv[0] is not None:
+            pos_offset = past_kv[0][0].size(2)
+            
+        x = self.pos_emb(x, pos_offset=pos_offset)
+
+        # Инициализация нового кеша
+        new_kv_cache = [] if past_kv is not None or T > 1 else None
+        current_past = past_kv
+
+        for i, block in enumerate(self.blocks):
+            layer_past = current_past[i] if (current_past and i < len(current_past)) else None
+            x, layer_kv = block(x, layer_past)
+            
+            if new_kv_cache is not None:
+                new_kv_cache.append(layer_kv)
+
+        x = self.ln_f(x)
+        logits = self.lm_head(x)
+        return logits, new_kv_cache
+
+    @torch.no_grad()
+    def generate(
+        self,
+        input_ids: torch.Tensor,
+        max_new_tokens: int = 100,
+        temperature: float = 0.8,
+        top_p: float = 0.95,
+        repetition_penalty: float = 1.0,
+        do_sample: bool = True,
+        eos_token_id: int = 50256
+    ) -> torch.Tensor:
+        kv_cache = [None] * NUM_LAYERS
+        current_ids = input_ids.clone()
+
+        for step in range(max_new_tokens):
+            if step == 0:
+                input_for_model = current_ids
+            else:
+                input_for_model = current_ids[:, -1].unsqueeze(-1)
+
+            logits, kv_cache = self(input_for_model, kv_cache)
+            next_token_logits = logits[:, -1, :]
+
+            if temperature > 0:
+                next_token_logits = next_token_logits / temperature
+
+            # Repetition Penalty (логика сохранена)
+            if repetition_penalty != 1.0:
+                for i in range(current_ids.shape[0]):
+                    unique_tokens = torch.unique(current_ids[i]).tolist()
+                    for token_id in unique_tokens:
+                        score = next_token_logits[i, token_id]
+                        if score < 0:
+                            next_token_logits[i, token_id] = score * repetition_penalty
+                        else:
+                            next_token_logits[i, token_id] = score / repetition_penalty
+
+            # Top-P сэмплирование (логика сохранена)
+            if do_sample and top_p < 1.0:
+                sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
+                cumulative_probs = torch.softmax(sorted_logits, dim=-1).cumsum(dim=-1)
+                sorted_indices_to_remove = cumulative_probs > top_p
+                sorted_indices_to_remove[:, 1:] = sorted_indices_to_remove[:, :-1].clone()
+                sorted_indices_to_remove[:, 0] = False
+                indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
+                next_token_logits = next_token_logits.masked_fill(indices_to_remove, float('-inf'))
+
+            # Сэмплирование
+            if do_sample and temperature > 0:
+                probs = torch.softmax(next_token_logits, dim=-1)
+                if torch.isnan(probs).any() or torch.isinf(probs).any():
+                    next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+                else:
+                    next_token = torch.multinomial(probs, num_samples=1)
+            else:
+                next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
+
+            if next_token.item() == eos_token_id:
+                break
+
+            current_ids = torch.cat([current_ids, next_token], dim=1)
+
+        return current_ids
+
+
+if __name__ == "__main__":
+    os.makedirs("models", exist_ok=True)
+
+    model = GPTPyTorch().to(device)
+    model.eval()
+
+    total_params = sum(p.numel() for p in model.parameters())
+    print(f"Device: {device}")
+    print(f"Total parameters: {total_params / 1e6:.2f}M") # ~157.14M
+
+    # 1. Проверка первого прохода (T=50)
+    input_ids_T50 = torch.randint(0, VOCAB_SIZE, (1, 50), device=device)
+    with torch.no_grad():
+        logits_50, kv_cache_50 = model(input_ids_T50)
+        expected_k_shape = (1, NUM_HEADS, 50, HEAD_DIM)
+        # Проверяем, что размерность головы HEAD_DIM (96) соблюдена
+        assert kv_cache_50[0][0].shape == expected_k_shape
+        print(f"Initial logits shape: {logits_50.shape}")
+        print(f"Initial KV-cache seqlen: {kv_cache_50[0][0].size(2)} (Head Dim: {kv_cache_50[0][0].size(3)})")
+
+    # 2. Проверка инкрементального прохода (T=1)
+    input_ids_T1 = torch.randint(0, VOCAB_SIZE, (1, 1), device=device)
+    with torch.no_grad():
+        logits_51, kv_cache_51 = model(input_ids_T1, past_kv=kv_cache_50)
+
+        # Проверка длины кеша: 50 + 1 = 51
+        assert kv_cache_51[0][0].size(2) == 51
+        print(f"Incremental logits shape: {logits_51.shape}")
+        print(f"Incremental KV-cache seqlen: {kv_cache_51[0][0].size(2)}")
+
+    # 3. Проверка функции generate
+    generated = model.generate(input_ids_T50, max_new_tokens=5, temperature=0.8, top_p=0.9)
+    print(f"Generated sequence length (50 + 5): {generated.shape[1]}")
+
+    save_path = "models/JiRack_GPT_L8_H8_PostNorm_fixed.pt"
+    torch.save(model.state_dict(), save_path)
+    print(f"Model successfully saved to {save_path}")